CH MaD Center for

Hierarchical 

Materials Design

ANNUAL REPORT 2015
On the cover: Simulation of out of equilibrium solidification of a faceted crystal

in the presence of thermal fluctuations.

The atomic probability density of the

crystal lattice is shown. Clearly shown is

the ledge nucleation and growth mechanism, a hallmark of faceted systems. See

Chapter 16 (Nana Ofori-Opoku, NISTCHiMaD Postdoctoral Research Fellow)

Editor

Emine Begum Gulsoy, Ph.D.

CHiMaD Associate Director

CHiMaD Headquarters

Northwestern University, O.T. Hogan Biological Sciences Building, Suite 1160

2205 Tech Drive, Evanston, IL, USA 60208

Ph: (847)467-6247 chimad.northwestern.edu | chimad@northwestern.edu

This work was performed under the following financial assistance award 70NANB14H012 from

U.S. Department of Commerce, National Institute of Standards and Technology as part of the

Center for Hierarchical Materials Design (CHiMaD).

The template used for this report was downloaded from: http://www.LaTeXTemplates.com, was

created by Mathias Legrand (legrand.mathias@gmail.com) with modifications by Vel

(vel@latextemplates.com). Further modifications were applied by the editor. This template is

licensed under CC BY-NC-SA 3.0 (http://creativecommons.org/licenses/by-nc-sa/3.0/).

CHiMaD 2015
Contents

I Center for Hierarchical Materials Design

1 Center for Hierarchical Materials Design . . . . . . . . . . . . . . . . . . . . . . . 11

II CHiMaD Research

2 Low-Dimensional Nanoelectronic Materials . . . . . . . . . . . . . . . . . . . . 15

2.1 Design Goals 15

2.2 Significant Accomplishments in 2015 16

2.3 Research Accomplishments 16

2.4 Collaborations 19

2.4.1 CHiMaD Collaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.4.2 NIST Collaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.4.3 External Collaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.5 Technology Transfer 20

2.6 Publications and Presentations 21

2.7 CHiMaD Team 21

3 Data Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.1 Design Goals 23

3.2 Significant Accomplishments in 2015 24

3.3 Research Accomplishments 24
3.4 Collaborations 27

3.4.1 CHiMaD Collaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3.4.2 NIST Collaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.4.3 External Collaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.5 Technology Transfer 28

3.6 Publications and Presentations 28

3.7 CHiMaD Team 28

4 In-Situ Silicon Composites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

4.1 Design Goals 31

4.2 Significant Accomplishments in 2015 32

4.3 Research Accomplishments 33

4.4 Collaborations 33

4.4.1 NIST Collaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

4.4.2 External Collaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

4.5 Publications and Presentations 34

4.6 CHiMaD Team 34

5 Directed Self-assembly of Block Copolymer Films for Lithography 35

5.1 Design Goals 35

5.2 Significant Accomplishments in 2015 36

5.3 Research Accomplishments 37

5.4 Collaborations 41

5.4.1 CHiMaD Collaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

5.4.2 NIST Collaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

5.5 Technology Transfer 42

5.6 Publications and Presentations 43

5.7 CHiMaD Team 43

6 Soft Matter Design Based on Charge Complexation . . . . . . . . . . . . 45

6.1 Design Goals 45

6.2 Significant Accomplishments in 2015 46

6.3 Research Accomplishments 48

6.4 Collaborations 50

6.4.1 NIST Collaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

6.4.2 External Collaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

6.5 Publications and Presentations 51

6.6 CHiMaD Team 52

7 Polymer Matrix Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

7.1 Design Goals 56

7.2 Significant Accomplishments in 2015 56

7.3 Research Accomplishments 56
7.4 Collaborations 62

7.4.1 NIST Collaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

7.4.2 External Collaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

7.5 Technology Transfer 63

7.6 Publications and Presentations 63

7.7 CHiMaD Team 63

8 Precipitation Strengthened Alloys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

8.1 Design Goals 65

8.2 Significant Accomplishments in 2015 66

8.3 Research Accomplishments 68

8.4 Collaborations 76

8.4.1 CHiMaD Collaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

8.4.2 NIST Collaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

8.4.3 External Collaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

8.5 Publications and Presentations 78

8.6 CHiMaD Team 78

9 Organic Bulk Heterojunction Polymer Solar Cells . . . . . . . . . . . . . . . 81

9.1 Design Goals 81

9.2 Significant Accomplishments in 2015 82

9.3 Research Accomplishments 83

9.4 Collaborations 90

9.4.1 CHiMaD Collaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

9.4.2 NIST Collaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

9.4.3 External Collaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

9.5 Publications and Presentations 91

9.6 CHiMaD Team 91

III CHiMaD Seed Groups

10 Additive Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

10.1 Significance & Research 95

10.2 Collaborations 96

10.2.1 CHiMaD Collaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

10.2.2 NIST Collaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

10.2.3 External Collaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

10.3 Publications and Presentations 96

10.4 CHiMaD Team 97

11 Coarse Grained Simulations of Organic Polymer Solar Cells . . . . . 99

11.1 Significance & Research 99
11.2 Collaborations 101

11.2.1 CHiMaD Collaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

11.3 Publications 101

11.4 CHiMaD Team 101

12 Impact Mitigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

12.1 Significance & Research 103

12.2 Collaborations 105

12.2.1 NIST Collaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

12.3 CHiMaD Team 105

IV CHiMaD Tools

13 Materials Data Facility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

13.1 Design Goals 109

13.2 Materials Data Facility 110

13.2.1 Publishing and Discovering Data with MDF . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

13.3 Future Outlook 111

13.4 NIST Collaborations 112

13.5 Technology Transfer 113

13.6 Publications and Presentations 113

13.7 CHiMaD Team 113

14 Phase Field Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

14.1 Research Tools 115

14.2 Collaborations 117

14.2.1 NIST Collaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

14.2.2 External Collaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

14.3 Publications and Presentations 117

14.4 CHiMaD Team 118

V Other

15 Honors & Awards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

16 NIST-CHiMaD Postdoctoral Research Fellows . . . . . . . . . . . . . . . . . . 123

17 CHiMaD Principal Investigators and Staff . . . . . . . . . . . . . . . . . . . . . . 129

17.1 CHiMaD Principal Investigators 129

17.2 CHiMaD Technical Staff 133

17.3 CHiMaD Administrative Staff 133
18 CHiMaD NIST Collaborators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

19 CHiMaD Outreach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

19.1 MGI Seminar Series 141

19.2 ASM Materials Genome Toolkit 144

19.3 Workshops and Meetings 146

19.4 Outreach to Under Represented Groups 152

19.5 Other Outreach Activities Organized by CHiMaD PIs 153

VI Information Dissemination

20 Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

21 Information Dissemination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161

21.1 Low-Dimensional Nanoelectronic Materials 161

21.2 Data Mining 162

21.3 In-Situ Si Composites 163

21.4 Soft Matter Design Based on Charge Complexation 163

21.5 Directed Self-Assembly of Block Copolymers for Lithography 165

21.6 Polymer Matrix Materials 165

21.7 Precipitation-Strengthened Alloys 167

21.8 Organic Bulk Heterojunction Polymer Solar Cells 168

21.9 Materials Data Facility 169

21.10 Additive Manufacturing 169

21.11 Coarse Grained Simulations of Organic Polymer Solar Cells 170

21.12 Phase Field Methods 170

21.13 ASM Educational Outreach 170

I

1 Center for Hierarchical Materials Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Center for Hierarchical

Materials Design

1. Center for Hierarchical Materials Design

Foreword

We are pleased to present the second annual report of the Center for Hierarchical Materials Design

(CHiMaD), the National Institute for Standards and Technology (NIST) Center of Excellence

in Advanced Materials. CHiMaD has become a focal point, both nationally and internationally,

for the Materials Genome Initiative (MGI). We are committed to achieving the goals of the MGI

through the development of broad research and outreach programs that involve participants from

universities, national laboratories and companies. Bringing together researchers with expertise in

computation, data science, and experiment to focus on the design of new materials is at the core

of both the MGI and the CHiMaD. These collaborative efforts show the power of incorporating

experimental and computational databases in the materials design process.

Along with scientists and engineers from the National Institute for Standards and Technology, we

are demonstrating the power and potential of the MGI by designing novel materials ranging from

cobalt alloys to polymers for photovoltaics. The design effort compasses 36 principal investigators,

34 postdoctoral research fellows, 38 graduate students and 7 undergraduate students working in

the CHiMaD use-case groups along with 3 technical staff members and 40 NIST collaborators.

This report presents the objectives and accomplishments of our use-case groups, seed groups, and

multiple tool development efforts. The breadth of our portfolio allows us to interact with a diverse

array of industrial partners. We are also a resource for the Nation through the sponsorship of

workshops, on topics such as community standard codes for phase field methods, materials design,

mutivalent interactions in polyelectrolytes, and industrial alloy design and development, as well as

by providing tools and databases, such as the Materials Data Facility (MDF), for the community.

The MDF will serve as a resource for sharing, storing, and mining the large datasets that are being

produced by state-of-the-art experimental facilities. Even though we are only in our second year,

CHiMaD’s research portfolio has evolved with the expansion of efforts in additive manufacturing

and a reduction of our work on Si-based structural alloys.

With our increasing national and international visibility and many design efforts that are moving

forward with great speed, we are making the promise of the Materials Genome Initiative a reality.

We look forward to another exciting year.

Peter Voorhees, Juan de Pablo, Gregory Olson

CHiMaD Co-Directors
12 Chapter 1. Center for Hierarchical Materials Design

Mission and Vision

Accelerating materials discovery and commercialization by design and development of hierarchical

methods and materials and enabling the complete integration of computation, experimentation and

databases by building a strong community of current and future researchers

Center for Hierarchical Materials Design (CHiMaD) is a NIST-sponsored center of

excellence for advanced materials research that is focused on developing the next generation

of computational tools, databases and experimental techniques in order to enable the

accelerated design of novel materials and their integration to industry, one of the primary

goals of the Materials Genome Initiative (MGI).

This Chicago-based consortium includes Northwestern University (NU) as the lead, University of Chicago (UChicago), Northwestern-Argonne Institute for Science and Engineering

(NAISE), a partnership between Northwestern University and Argonne National Laboratory (ANL), and the Computational Institute (CI), a partnership between University

of Chicago and Argonne National Laboratory. The consortium is also partnered with

Questek Innovations (QT), a pioneering materials design company, ASM International and

Fayetteville State University.

Designing novel materials of specific properties for a particular application requires

simultaneously utilizing physical theory, advanced computational methods and models,

materials properties databases and complex calculations. This approach stands in contrast

to the traditional trial-and-error method of materials discovery. CHiMaD aims to focus this

approach on the creation of novel hierarchical materials which exploit distinct structural

details at various scales, from the atomic on up, to obtain enhanced properties. The center’s

research focuses on both organic and inorganic advanced materials in fields as diverse as

self-assembled biomaterials, smart materials for self-assembled circuit designs, organic

photovoltaic materials, advanced ceramics and metal alloys.

Objectives

• To foster hierarchical materials discovery, in accordance with the goals of MGI

and NIST, by developing the next generation of computational tools, databases and

experimental techniques.

• To connect and convene the current experts and the next generation of scientists by advancing the multi-disciplinary and multi- sector communication through workshops,

seminars, training opportunities and meetings

• To provide opportunities to transition new breakthroughs in advanced materials to

industry

• To adopt a seamless integration of prediction, measurement and interpretation

• To maintain and advance the collective knowledge on material systems and methodologies and to serve, together with NIST, as a national resource for verified codes

and curated databases that will enable proliferation of a materials-by-design strategy

and materials discovery through US industry partners

• To build on the established success in metal alloy systems to prove generality of

the materials- by-design approach through application to both inorganic and organic

advanced systems for both structural and multifunctional applications

• To expand the 3D and 4D characterization methods for hard materials design and to

bring such methods to the forefront of soft materials design
II

2 Low-Dimensional Nanoelectronic Materials . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3 Data Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

4 In-Situ Silicon Composites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

5 Directed Self-assembly of Block Copolymer Films for Lithography . . . . . 35

6 Soft Matter Design Based on Charge Complexation . . . . . . . . . . . . . . . . . . 45

7 Polymer Matrix Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

8 Precipitation Strengthened Alloys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

9 Organic Bulk Heterojunction Polymer Solar Cells . . . . . . . . . . . . . . . . . . . . . 81

CHiMaD Research

2. Low-Dimensional Nanoelectronic Materials

Mark Hersam (NU), Lincoln Lauhon (NU)

Significance

The Low-Dimensional Nanoelectronic Materials use-case group seeks to understand and

realize p-type and n-type doping in the low-dimensional limit. Since low-dimensional

nanoelectronic materials have exceptionally high surface area to volume ratios, doping can

be realized via two qualitatively different pathways: conventional substitutional doping and

surface chemical functionalization. Furthermore, contacts can be designed to selectively

inject carriers into the conduction or valence band, thus providing effective n-type and

p-type behavior, respectively. Experimental methods in this use-case group include charge

transport measurements, optical spectroscopy (e.g., absorbance, photoluminescence, and

Raman spectroscopy), and high-resolution microscopy (e.g., scanning probe microscopy,

electron microscopy, and atom probe tomography). These experimental methods are

complemented by an extensive suite of computational techniques including multi-scale

modeling, molecular dynamics, density functional theory, and finite element methods. The

interplay between experiment and computation accelerates the understanding and design

of doped low-dimensional nanoelectronic materials and their heterostructures.

2.1 Design Goals

The Low-Dimensional Nanoelectronic Materials Use-Case Group is pursuing the following

design goals:

Substitutional Doping of Low-Dimensional Semiconductors. Substitutional doping is the

most common means of controlling carrier concentration in bulk semiconductors. In the

low-dimensional limit, substitutional doping is expected to play the same role, although

the reduced degrees of freedom for charge transport suggest that this doping strategy will

have greater deleterious effects on charge carrier mobility than in bulk materials. By

combining unique characterization methods (e.g., atom probe tomography) with advanced

computational methods (e.g., density functional theory), the design trade-offs between
16 Chapter 2. Low-Dimensional Nanoelectronic Materials

carrier concentration and mobility can be quantitatively understood and exploited to

optimize materials properties for low-dimensional nanoelectronic devices.

Surface Chemical Functionalization Doping of Low-Dimensional Semiconductors. Since

low-dimensional semiconductors have exceptionally high surface area to volume ratios,

carrier concentration can also be controlled via surface chemical functionalization doping.

The vast phase space for candidate surface chemical treatments necessitates an MGI-based

approach to this design problem. Specifically, electron withdrawing and electron donating

chemistries are being screened experimentally and computationally to realize controlled ptype and n-type doping, respectively. The results of this work are informing ongoing efforts

to optimize spatially varying doping profiles that serve as the basis of many nanoelectronic

devices including p-n junction diodes and transistors.

Developing Heterojunctions Consisting of Low-Dimensional Semiconductors. Since lowdimensional semiconductors have self-passivating surfaces, they can be directly integrated

into van der Waals heterostructures without the epitaxial constraints of bulk semiconductors.

Since this attribute implies a much greater number of heterojunction possibilities compared

to bulk materials, efficient experimental and computational screening methods are required.

Specific design goals include engineering of the band alignment and charge/energy transfer

across heterojunction interfaces, which will allow optimization of nanoelectronic device

metrics such as anti-ambipolarity, operating frequency, and optoelectronic response.

2.2 Significant Accomplishments in 2015

Atomically thin MoS2/graphene heterostructures are promising candidates for nanoelectronic and optoelectronic technologies. The realization of rotationally commensurate

MoS2 on epitaxial graphene (EG) provides an ideal platform for fundamental studies

of low-dimensional heterojunctions and their applications. In addition to the inherent

advantages of van der Waals epitaxy (e.g., reduced defect density, sharper interfaces, and

consistent material quality), the registry between the MoS2 and EG allows for the study

of crystal orientation-dependent properties as well as specific types of grain boundaries

that affect electronic and optoelectronic performance. Towards this end, the crystalline

ordering of EG on SiC was exploited to template the growth of monolayer MoS2 crystal

domains by chemical vapor deposition, realizing a transfer-free rotationally commensurate

MoS2/graphene heterostructure with controlled thickness. The rotational ordering of MoS2

domains observed via local scanning probe microscopy was confirmed to persist over

the entire EG substrate by synchrotron X-ray scattering measurements, with preferential

alignment of the MoS2 along the EG lattice direction with matching six-fold symmetry.

Electronic characterization of the heterostructure using scanning tunneling spectroscopy

revealed a MoS2 band gap of 2 eV and edge effects leading to band gap narrowing to

0.4 eV in close agreement with DFT calculations. Raman spectroscopy and synchrotron

X-ray scattering determined the monolayer MoS2 to be strain-free. This work was recently

published in ACS Nano [1].

2.3 Research Accomplishments

Substitutional Doping of Low-Dimensional Semiconductors. The location of substitutional

dopants in two-dimensional materials was determined by Lauhon using atom probe

tomography (APT). Ag-doped (PbSe)5(Bi2Se3)3 was studied due to its tunable interlayer
2.3 Research Accomplishments 17

bonding and previously unknown distribution of dopants between Pb and Bi layers. The

APT reconstruction resolved Bi and Pb layers (Figure 2.1a), and spatial distribution

maps (Figure 2.1b) indicated that Ag dopants are located in both layers, contrary to the

expectation of substitution for Pb alone. Density functional theory calculations by NIST

confirms that a distribution of Ag between both layers is energetically most favorable

(Figure 2.1c). The precise determination of dopant distribution will advance understanding

of how Ag doping changes (PbSe)5(Bi2Se3)3 from a metal to a superconductor. In addition,

this work demonstrated the potential for APT to analyze the dopant distribution of twodimensional materials, especially in the layer-stacking direction. Beyond Ag, Cu-doped

Bi2Se3 was also analyzed. Cu doping can make Bi2Se3 superconducting, but the location

of Cu atoms is still under debate. The spatial distribution map of Cu based on APT

analysis suggested that Cu atoms are incorporated in both substitutional and intercalated

sites (Figure 2.1d). With further refinement, this analysis may elucidate the origin of

superconductivity in Cu-doped Bi2Se3.

Figure 2.1: (a) A three-dimensional reconstruction of (PbSe)5(Bi2Se3)3 shows layered structures.

(b) SDM of Bi, Pb, Se, and Ag relative to Bi. (c) DFT calculation of defect formation energies of

Ag in different locations. (d) SDM of Cu relative to Bi in Cu-doped Bi2Se3.

Surface Chemical Functionalization Doping of Low-Dimensional Semiconductors. Fewlayer black phosphorus (BP) is an emerging two-dimensional nanoelectronic material that

possesses a tunable band gap as a function of thickness, high charge carrier mobilities of

up to 1000 cm2/V-s, and highly anisotropic properties. However, BP is chemically reactive

and degrades rapidly in ambient conditions. In an inter-use-case group collaboration with

Marks, Hersam has utilized aryl diazonium functionalization for controlled p-type doping

of BP. In particular, mechanically exfoliated few-layer BP was covalently functionalized
18 Chapter 2. Low-Dimensional Nanoelectronic Materials

Figure 2.2: AFM height image of MoS2 crystal domains on EG on SiC. The inset depicts STM

atomic resolution images of the graphene and MoS2. The right panel shows the Qxy 2D reciprocal

space map of MoS2/EG projected from Qz = 0.08 Å

−1

to 0.12 Å

−1 by synchrotron X-ray scattering.

The b* reciprocal space vectors are indicated by arrows. The MoS2 and EG vectors coincide,

demonstrating rotational alignment of the two materials.

with 4-nitrobenzene diazonium and 4-methoxybenzene diazonium. This chemical modification of few-layer BP results in a controllable p-type doping effect, improving both

the BP field-effect transistor device ON/OFF ratio and hole carrier mobility. Furthermore,

atomic force microscopy (AFM) measurements demonstrate invariant morphology of the

BP after 10 days of ambient exposure, confirming the passivating nature of the diazonium

chemistry. The improved stability and tailorable p-type doping of few-layer BP using aryl

diazonium functionalization advances its prospects as a low-dimensional semiconductor

for nanoelectronic applications.

Developing Heterojunctions Consisting of Low-Dimensional Semiconductors. In an interuse-case group collaboration with Bedzyk, Hersam has realized the synthesis of lowdimensional heterojunctions through the rotationally commensurate growth of atomically

thin molybdenum disulfide (MoS2) on a substrate of epitaxial graphene (EG) on silicon

carbide (SiC). The crystalline ordering of the EG substrate was exploited to template

the growth of MoS2 by chemical vapor deposition (CVD), giving rise to a transfer-free

two-dimensional heterostructure with registry between its constituent materials (Figure

2.2). The electronic and structural properties of the MoS2/EG heterostructure were investigated via a comprehensive suite of techniques including AFM, ultra-high vacuum

scanning tunneling microscopy and spectroscopy (STM/STS), in situ X-ray photoelectron

spectroscopy (XPS), Raman spectroscopy, and synchrotron X-ray scattering. In addition to

providing an ideal platform for fundamental low-dimensional heterostructure studies, this

work suggests that EG is a promising substrate for the van der Waals epitaxial growth of

other emerging two-dimensional nanoelectronic materials.
2.4 Collaborations 19

2.4 Collaborations

2.4.1 CHiMaD Collaborations

Anti-Ambipolar Semiconductor Heterojunctions

Tobin Marks (NU), Lincoln Lauhon (NU), and Mark Hersam (NU)

Gate-tunable p-n junctions are realized from semiconductor heterojunctions. These devices

show anti-ambipolar transfer curves. This work resulted in the following collaborative

publication, [2].

Rotationally Commensurate Growth of MoS2 on Epitaxial Graphene

Michael Bedzyk (NU), Mark Hersam (NU)

Rotationally commensurate growth of MoS2 on epitaxial graphene is realized via chemical

vapor deposition. This work resulted in the following collaborative publication, [1].

2.4.2 NIST Collaborations

FET Fabrication and Characterization of MoTe2 2H and 1T Bulk Crystal Samples

Albert Davydov (NIST), Mark Hersam (NU), Francesca Tavazza (NIST), Arunima Singh

(NIST)

Davydov has developed a CVT sysnthesis of both 2H and 1T phases of MoTe2. Hersam

is mechanically exfoliating these bulk crystals, fabricating FETs, and performing transport

measurements to confirm the semiconducting and metallic nature of the 2H and 1T phases,

respectively. Tavazza and Singh are providing computational support for this effort.

Effect of MoS2 Stoichiometry on Morphology of MoS2 Flakes During Growth

Lincoln Lauhon, Francesca Tavazza (NIST), Arunima Singh (NIST)

The effect of Mo/S availability during MoS2 growth on the resulting MoS2 crystal shape

is being investigate by Singh and Tavazza using first-principles simulations. This work

complements Lauhon et al. effort on CVD growth of MoS2 providing interpretation of its

results. Energetic evaluations of island of different shapes have been conducted, as well

as corresponding determination of charge rearrangement and vacancy formation energy.

Density of state analysis of the many investigated configurations is currently ongoing.

Device Fabrication and Testing of Large-area MoS2 Thin Films

Mark Hersam (NU), Lincoln Lauhon (NU), Albert Davydov (NIST)

Hersam and Lauhon have recently published work on the memristive behavior of substoichiometric monolayer MoS2 with grain boundaries. The highly polycrystalline nature

of the NIST MoS2 thin films fabricated from sulfurization of MoO3 by Davydov may

allow for large-scale fabrication of gate-tunable memristors. Hersam is fabricating FETs

using the Davydov MoS2 films and checking for the presence of bi-stable switching at

high source-drain bias.

First Principles Calculations of Doping in Layered Compounds

Lincoln Lauhon (NU), Francesca Tavazza (NIST), Arunima Singh (NIST)

First-principles simulations were performed by Singh and Tavazza to complement the

efforts of Lauhon et al. to map the location of substitutionally doped Ag atoms in

(PbSe)5(Bi2Se3)3m(m = 1,2,...), a homologous series of compounds consisting of 2D heterostructures of Bi2Se3 layers and PbSe layers. First principles simulations of defect

formation energy and temperature dependent probabilities of an ensemble of substitutionally doped Ag-(PbSe)5(Bi2Se3)3 reveal that the Ag exists predominantly in both PbSe

layers and Bi2Se3 layers up to temperatures as high as 1000 K. The computational results

agree well with ATP measurements, both confirming that Ag exists in both PbSe layers and
20 Chapter 2. Low-Dimensional Nanoelectronic Materials

Bi2Se3 layers. In future, such symbiotic experimental and computational investigations can

be used for accurate determination of dopant locations in 2D materials; revealing structureproperty relations and resulting in the development of controlled growth techniques for

engineering materials properties.

Chemical Vapor Transport Growth of Transition Metal Dichalcogenides and Alloys

Lincoln Lauhon (NU), Albert Davydov (NIST), Sergiy Krylyuk (NIST)

Upon request from the Lauhon group, Davydov and Krylyuk have grown a set of singlecrystalline transition metal dichalcogenides (TMDC) platelets, including MoTe2 and WTe2

crystals grown by chemical vapor transport. The Lauhon group is currently testing these

TMDC crystals to design the sample-prep methodology suitable for analysis using atom

probe tomography at Northwestern University. Additional collaboration on synthesis and

characterization is planned.

2.4.3 External Collaborations

Anti-Ambipolar Semiconductor Heterojunctions

Mark Hersam (NU), Chris Kim (University of Minnesota)

Gate-tunable p-n junctions are realized from semiconductor heterojunctions. These devices

show anti-ambipolar transfer curves. This work resulted in the following collaborative

publication, [2].

Doping Black Phosphorus via Chemical Modification

Mark Hersam (NU), George Schatz (NU)

Two-dimensional black phosphorus is realized via functionalization with diazonium chemistry. This work is currently being prepared for publication: C. R. Ryder, J. D. Wood, S. A.

Wells, Y. Yang, D. Jariwala, T. J. Marks, G. C. Schatz, and M. C. Hersam, "Covalent functionalization and passivation of exfoliated black phosphorus via aryl diazonium chemistry,”

in preparation, 2015.

Growth of Ag doped (PbSe)5(Bi2Se3)3 and Cu doped Bi2Se3

Lincoln Lauhon (NU), Mercouri Kanatzidis (NU)

Kanatzidis group provides samples of Ag doped (PbSe)5(Bi2Se3)3 and Cu doped Bi2Se3.

Lauhon group is characterizing these layered 2D materials using atom probe tomography

to study the influence of interlayer bonding strength.

2.5 Technology Transfer

Invention

System and method for anti-ambipolar heterojunctions from solution-processed semiconductors

D. M. Jariwala, V. K. Sangwan, W. Xu, H. Kim, T. J. Marks, M. C. Hersam

U.S. Provisional Patent application filed on 1/9/15

Patent Number: 62/101, 676

This invention describes a method for forming large-area, anti-ambipolar p-n heterojunctions using solution-processed p-type semiconducting single-walled carbon nanotubes

(s-SWCNTs) and n-type amorphous indium gallium zinc oxide (a-IGZO) thin films via

photolithographic and etching techniques
2.6 Publications and Presentations 21

2.6 Publications and Presentations

2

Number of CHiMaD-supported publications in 2015 by Low-Dimensional Nanoelectronic Materials use-case group. Please see chapter 20 for details. [1, 2]

25 Number of presentations on CHiMaD supported research in 2015, please seesection 21.1 for the complete list.

2.7 CHiMaD Team

Low-Dimensional Nanoelectronic Materials

Use-Case Group Researchers

Name Position Affiliation Project (PI)

Mark Hersam Principal Investigator NU/MSE

Lincoln Lauhon Principal Investigator NU/MSE

Hadallia Bergeron Graduate Student NU/MSE Low-dimensional semiconductor

heterojunctions (Hersam)

Xiaochen Ren Graduate Student NU/MSE Substitutional doping and alloy

formation in transition metal

dichalcogenides (Lauhon)

Header Image: Ultra-high vacuum scanning tunneling microscopy image of epitaxial graphene, which is used as a

growth substrate for MoS2. Figure adapted from reference

[1]

3. Data Mining

Alok Choudhary (NU), Ankit Agrawal (NU), Gregory Olson (NU), Chris Wolverton

(NU), Wei Chen (NU)

Significance

The CHiMaD data mining team is working towards making significant advances in datadriven informatics to accelerate materials discovery. In this era of big data, we are being

bombarded with huge volumes of data from a variety of different sources (experiments,

simulations) at unprecedented rates in practically all fields of science and engineering,

and materials science is no exception. Advanced techniques for data-driven analytics

are needed to analyze all this data in ways that can help extract meaningful information

and knowledge from it, and thus contribute to accelerating materials discovery and the

vision of Materials Genome Initiative. The diversity and expertise within the CHiMaD

team provides unique opportunities for collaborations among materials scientists and

computer scientists to realize this goal. This requires designing and developing specific

analytics and data mining approaches for problems in materials science, and evaluating

the resulting insights/discoveries via necessary experiments and simulations. The longterm vision is to work with NIST to enable data-to-discovery (or actionable insights) at

unprecedented latency through summary statistics, query, reporting, analysis, bottom-up

as well as hypothesis-driven discoveries and data mining on various heterogeneous and

big databases that are complex, high dimensional, structured and semi-structured.

3.1 Design Goals

The design goals of the Data Mining use-case group for this reporting period were threefold:

• Continue to make progress in the materials informatics projects started last year

• Initiate new projects within those collaborations where possible

• Explore other important problems in materials science that can benefit from datadriven informatics, and try to kick-off new collaboration(s) both within and outside

the CHiMaD team, and also with NIST researchers.
24 Chapter 3. Data Mining

The ongoing projects from last year include our efforts towards integrating CALPHAD and

data mining for predicting the fatigue strength of steel for advanced steel design, identifying

significant microstructure descriptors for microstructure representation and design, and

extending our composition-based machine learning framework to computationally discover

new materials with desired properties. Further, we have started a new project in which we

try to take into account the characteristics of the crystal structure in addition to composition

information, for the purpose of predicting properties of materials. This year we have also

started a new collaboration with NIST researchers that aims at automating the currentlymanual process of classification of journal articles as being relevant or not relevant for

NIST’s Thermodynamic Research Center (TRC) team to extract key data from it, as part

of their data curation effort. Finally, we have recently embarked upon discussions with

the CHiMaD OPV use-case group for a potentially new collaboration exploring the use of

data mining to design and develop organic photovoltaics, and are currently looking to find

an appropriate dataset to kick-off our data mining efforts in this arena.

3.2 Significant Accomplishments in 2015

Improved Prediction of Fatigue Strength of Steel. The team of Agrawal, Choudhary,

Olson, and Xiong (NU) have integrated a steel process-property database from NIMS with

CALPHAD database, and constructed accurate predictive models for fatigue strength of

steel which are more widely applicable than prior models based on process and composition.

Framework for Discovery of New Metallic Glasses. In collaboration with Agrawal, Choudhary, and Wolverton, Ward (NU) has further developed composition-based machine

learning framework to computationally discover new metallic glasses.

Improved Microstructural Descriptors. In collaboration with Chen and Dan Apley (IEMS

department, NU), Zhang (NU) has developed a Structural Equation Modeling (SEM) approach to identify significant microstructure descriptors for microstructure characterization

and reconstruction.

Improved Prediction of Formation Energy of New Compounds. In collaboration with

Agrawal, Choudhary, and Wolverton, Ward (NU), Liu (NU), Krishna (NU), and Hegde

(NU) have developed a technique based on Voronoi tessellations to incorporate the crystal

structure in the machine learning model for predicting the formation energy of new

compounds.

Development of Accurate Topic Modelling-based Classifier. A new collaboration between

Agrawal, Choudhary, Krishna (NU), and NIST researchers Dima (NIST), Kroenlein

(NIST), Padi (NIST), and Peskin (NIST), has developed an accurate topic modeling based

classifier for scientific journal articles to support the ongoing data curation effort at the

NIST Thermodynamic Research Center.

3.3 Research Accomplishments

Integrating CALPHAD and Data Mining for Advanced Steel Design. This work involving a

collaboration of Agrawal, Choudhary, Olson, and Xiong(NU), aims to design an effective

interface between data mining technique and CALPHAD methodology for materials

genomic design. The research studies are performed on the carbon steels fatigue strength

data with raw data sets obtained from experiments provided by NIMS (National Institute
3.3 Research Accomplishments 25

Figure 3.1: Overall schematic of the integration of NIMS process-property database and CALPHAD

modeling, and extracting process-structure-property linkages.

for Materials Science, Japan). The CALPHAD method is widely adopted as one of the

most powerful tools for designing new materials and processing heat treatment. Therefore,

it is critical to further develop CALPHAD method to study process-structure-property

relationships. As a start, data-mining model and CALPHAD database were coupled using

the in-house code through TQ-interface in the Thermo-Calc software package (Figure 3.1).

The machine learning process indicated that martensite-start (Ms) temperature is the most

critical parameter among the five thermodynamic parameters. Therefore, this year a new

model for calculating Ms temperature was investigated based on statistical analysis on

experimental databases, which takes less elemental influence into account. It was found

that the new model not only gives better estimates of Ms temperature but also in turn

of fatigue strength, which is quite promising and further approves the sensitivity of Ms

temperature for fatigue resistance. CALPHAD based data mining models gave excellent

prediction accuracy for fatigue strength with R2 values of around 0.9, which although is

lower than prior models, has the advantage of not being directly dependent of composition

parameters, and thus are more widely applicable for new steel compositions. A manuscript

on this work is in-preparation.

Composition-based Machine Learning Framework for Predicting Inorganic Material

Properties. This collaboration between Agrawal, Choudhary, Wolverton, and Ward

(NU) aims at creating an extensive set of composition-based parameters of materials

that could be coupled with data mining to effectively predict a wide variety of material

properties. Building upon our efforts from last year, we have focused this year on using

our composition-based machine-learning framework to predict new materials, in particular

metallic glass alloys, which possess a unique blend of properties from both metallic and

amorphous materials. For training the machine learning model, we used experimental

measurements taken from Nonequilibrium Phase Diagrams of Ternary Amorphous Alloys’

a volume of the Landolt-Börnstein collection. A machine learning model was created

to predict whether it is possible to form a metallic glass alloy at a certain composition

using melt spinning. Without any specific modifications for metallic glasses, we found that
26 Chapter 3. Data Mining

Figure 3.2: (left) Experimentally-measured regions of glass-forming ability in the Al-Ni-Zr ternary

system. (right) Predicted glass-forming ability by a machine learning model that was trained

without any data from the diagram present on the left.

models created using our framework are 90% accurate in cross-validation tests. To further

evaluate the ability of the model to extrapolate to new alloy systems, we devised a test

where we intentionally omitted an entire phase diagram from the training data and then

evaluated the performance of the model in that system. As shown in Figure 3.2, a machine

learning model trained without any data from the Al-Ni-Zr ternary system could accurately

predict the existence, locations, and relative sizes of the two glass-forming regions. This

result makes us confident that we can use this model to evaluate new chemical systems.

This framework has also been shown to be effective in predicting band gap of a compound,

which is crucial for discovery of semiconductor materials. A manuscript on this work is

in-preparation.

Supervised Learning-based Microstructure Characterization and Reconstruction. In collaboration with Chen and Daniel Apley (IEMS department, Northwestern University),

Zhang (NU) has developed a Structural Equation Modeling (SEM) approach to identify

significant microstructure descriptors with the least dependency. The method addresses descriptor redundancy in the existing approach and provides insight into the underlying latent

factors for categorizing microstructure. The effectiveness of the approach is demonstrated

and validated through confirmation of the reconstructed images and simulated material

properties of the epoxy-nanosilica system.

Fast Models for Properties of Crystalline Compounds Using Voronoi Tessellations and Machine Learning. This is a collaborative work between Agrawal, Choudhary, Wolverton,

and Ward (NU), Liu (NU), Krishna (NU), and Hegde (NU), where the goal is to effectively

incorporate crystal structure information into machine learning models to predict material

properties. The technique involves the use of Voronoi Tessellations to define the nearest neighbors of each atom, and then evaluating characteristics such as the coordination

number and the difference in elemental properties between the atom and its neighbors.

Subsequently attributes are created that describe the entire crystal by computing statistics

of the distribution of local environments over all atoms. The final step is to employ machine

learning to link these attributes to the property of the material. The first application for this

technique has been to predict the formation energy of crystalline compounds using data

from the OQMD. We have benchmarked our technique against two other machine learning

approaches and found that our models can be about twice as accurate (Figure 3.3).

Classification of Scientific Journal Articles to Support NIST Data Curation Efforts This
3.4 Collaborations 27

Figure 3.3: Comparison of the accuracy (in terms of error) of the Voronoi-tessellation-based

technique we are developing to the Coulomb Matrix (CM) [Int. J. Quantum Chem. 115, 1094

(2015)] and Partial Radial Distribution Function (PRDF) [Phys. Rev. B 89, 205118 (2014)]

approaches. Our model predicts the formation energy of crystalline compounds about twice as

accurately as these approach for most training set sizes.

new collaboration between Agrawal, Choudhary, Krishna (NU), and NIST researchers

Dima (NIST), Kroenlein (NIST), Padi (NIST), and Peskin (NIST), is aimed at supporting

the data curation effort of the NIST Thermodynamic Research Center (TRC). Its current

curation process is largely manual that requires each journal article to be examined and

evaluated for relevance before trying to extract data from it. In the current work, a

topic modeling based binary classifier was developed using a corpus of 2357 articles

where the relevancy of the article was manually pre-assigned. Out of 2357 articles, 1172

were relevant, and 1185 were not relevant. Topic modeling was done based on LDA

(Latent Dirichlet Allocation) with hyper parameter optimization. The resulting topics

were used as a training set for machine learning algorithms capable of doing binary

classification. The best cross-validation accuracy obtained, in terms of area under the

ROC curve, was 0.95 on 2357 articles dataset with 1000 topics (for each article). Further,

an online tool deploying the classifier is under construction (beta version available at:

http://info.eecs.northwestern.edu/TRCArticleClassifier).

3.4 Collaborations

3.4.1 CHiMaD Collaborations

Data Mining for Design and Development of OPV Materials

Ankit Agrawal (NU), Alok Choudhary (NU), Christopher Wolverton (NU), Luping Yu

(UC), Guilia Galli (UC)

During the CHiMaD Materials Design Workshop I use-case presentation by Goldey (UC)

and Schneider (UC), a possible collaboration between the Organic Bulk Heterojunction

Polymer Solar Cells and Data Mining use-case groups came to light. After initial discussions between use-case groups including Goldey (UC), Schneider (UC), Ward (NU),

Furmanchuk (NU), Paul (NU), it was decided that there is little correlation between device

efficiency and energy levels of polymeric material, but stronger correlations between power
28 Chapter 3. Data Mining

conversion efficiency and morphology-dominated properties.The groups will be exploring

the possibility of using data mining for determining the uncontrolled morphology of active

layer in order to support building of OPV devices. See section ?? for more details.

3.4.2 NIST Collaborations

Classification of Scientific Journal Articles to Support NIST Data Curation Efforts

Ankit Agrawal (NU), Alok Choudhary (NU), Alden Dima (NIST), Kenneth Kroenlein

(NIST), Sarala Padi (NIST), Adele Peskin (NIST)

This collaboration is aimed at supporting the data curation effort of the NIST Thermodynamic Research Center (TRC) by classifying an article as relevant or not relevant to their

data extraction pipeline. An online tool deploying the classifier is under construction (beta

version available at: http://info.eecs.northwestern.edu/TRCArticleClassifier)

3.4.3 External Collaborations

Structural Equation Modeling

Wei Chen (NU), Dan Apley (NU)

Through this collaboration, Chen and Apley are developing the supervised based learning

algorithm for microstructure characterization and reconstruction.

3.5 Technology Transfer

Software/Web-Tool

TRC Article Classifier

A. Khrishna, A. Agrawal, A. Choudhary

Access Link (Beta): http://info.eecs.northwestern.edu/TRCArticleClassifier

This is a software tool deploying the models for classfying a journal article as relevant or

not relevant to NIST Thermodynamics Research Center’s ongoing data curation efforts. It

is based on unsupervised topic modeling followed y supervised classification.

Software

Materials Agnostic Platform for Informatics and Exploration (Magpie)

L. Ward, C. Wolverton

Access Link (Beta): https://bitbucket.org/wolverton/magpie

Magpie is a software package designed to simplify the process of using machine learning to

develop new materials. This software includes the ability to compute attributes of materials,

train and validate models, and predict the properties of new materials all through a simple

command-line interface. Magpie also makes it possible to share results with other research

groups by creating interactive webpages or saving models in a system independent format.

3.6 Publications and Presentations

11 Number of presentations on CHiMaD supported research in 2015, please seesection 21.2 for the complete list.

3.7 CHiMaD Team
3.7 CHiMaD Team 29

Data Mining

Use-Case Group Researchers

Name Position Affiliation Project (PI)

Alok Choudhary Principal Investigator NU/EECS

Ankit Agrawal Principal Investigator NU/EECS

Gregory Olson Principal Investigator NU/MSE

Christopher Wolverton Principal Investigator NU/MSE

Wei Chen Principal Investigator NU/ME

Wei-keng Liao Research Faculty NU/EECS (Choudhary, Agrawal)

Wei Xiong Research Associate NU/MSE CALPHAD Databases (Olson)

Al’ona Furmanchuk Research Associate NU/EECS Data mining for design and development of OPV Materials

(Choudhary, Agrawal)

Amar Krishna Graduate Student NU/EECS Fast models for properties of

crystaline compounds using

Voronoi tessalation and machine

learning (Choudhary, Agrawal)

Rouqian Liu Graduate Student NU/EECS Fast models for properties of

crystaline compounds using

Voronoi tessalation and machine

learning (Choudhary, Agrawal)

Arindam Paul Graduate Student NU/EECS (Choudhary, Agrawal)

Logan Ward Graduate Student NU/MSE Machine Learning for Materials

Discovery (Wolverton)

Yichi Zhang Graduate Student NU/ME Microstructure Characterization

and Reconstruction (Chen)

Header Image: Datamining chart provided by (Agrawal).

4. In-Situ Silicon Composites

Peter Voorhees (NU), Christopher Wolverton (NU)

Significance

One of the major objectives of the CHiMaD is to transfer the knowledge of materials

design to industry. In support of this goal, the Si-alloys use-case group was undertaken

in close partnership with the Dow-Corning Corporation. The collaboration with Dow

Corning scientists was very close, with a significant number of the use-case group members

at Dow Corning. During the course of this work the in-situ Silicon Composites usecase group: established CALPHAD databases relevant to the design of these materials,

showed that ab-initio molecular dynamics can be used to provide accurate liquid diffusion

coefficients in these high temperature alloys and to create a liquid diffusion database,

developed a new model for eutectic solidification in multicomponent alloys that employed

the CALPHAD and liquid diffusion databases, developed a molar volume database that

was used successfully to design alloys that minimized cracking upon solidification, and

showed that Sr additions alone do not alter the morphology of the Si plates found in Si-Cr

eutectics thus establishing that the classical model for effects of Sr on Si-plate morphology

does not hold. These databases and models were combined with proprietary casting codes

and Dow-Corning databases to predict the microstructure of Si castings. Unfortunately,

Dow Corning decided to discontinue the Si alloys project, and thus we will be ending the

Si-alloys use-case group in CHiMaD.

4.1 Design Goals

The over-arching goal of the project is to the design of melt-castable ceramic materials,

and to design high performance Si-based composite materials for structural applications.

The focus of the use-case group is on Si-Cr-Ti-Co alloys. A major challenge with these

materials is that the Si is a strong compound former. Since both Si, at room temperature,

and Si compounds are brittle phases, the challenge is to design a polyphase microstructure

that increases the toughness of the materials beyond that of the constituent phases. Thus,

CALPHAD modeling is needed to choose the alloy compositions and predict the phases
32 Chapter 4. In-Situ Silicon Composites

present for a given alloy composition. The required toughness of the composite can be

attained through a fine spatial dispersion of one brittle phase in another. This polyphase

mixture should have microstructural features sufficiently small, and thus interfacial area

sufficiently high, to yield significant crack deflection. Such crack deflection is well known

to yield a tough material using brittle component phases. The fine distribution of the phases

will be created via solidification of eutectic or near-eutectic alloys. Thus, models of the

eutectic solidification process are needed to design the processing path and molds to yield

these fine microstructures. Finally, since Si expands on freezing, it is necessary to choose

alloy compositions that minimize solidification induced cracking. The goal is to produce

to produce a castable-ceramic with a toughness in the 4-6 MPa m1/2range.

4.2 Significant Accomplishments in 2015

Eutectic Growth in Two-Phase Multicomponent Alloys. A theory of two-phase eutectic

growth for a multicomponent alloy has been formulated. Using the local equilibrium

hypothesis, a theory for eutectic growth of an alloy with N elements has been developed.

We show that the growth law determined by Jackson and Hunt for binary eutectics can

be generalized to alloys with N elements. We find that the spacing of the eutectic for a

given interfacial velocity can be changed dramatically with the addition of a slow diffusing

element, or if the slope of the liquidus of the ternary addition is different from that of the

binary elements, see Figure 4.1. All of the quantities necessary to determine the change

in the eutectic spacing can be derived from a CALPHAD model of the multicomponent

thermodynamics.

We now have a model that can be used to guide the design of multicomponent alloys to

yield a given eutectic lamellar spacing. The spacing of the lamellar is a key factor in setting

the properties of eutectic alloys. A detailed publication is under preparation.

Figure 4.1: The variation of the product of the spacing of the eutectic microstructure λt and the

interfacial velocity V with the addition of element 3 compared to that of the symmetrical binary

alloy λb.∆C3 is the difference in concentrations of component 3 between the two solid phases, α and

β. Dashed lines correspond to: m

β

3 = −9K/(at%),m

α

3 = −10K/(at%), solid lines correspond to:

m

β

3 = 10K/(at%) and m

α

3 = −10K/(at%),, where mi

is the slope of the liquidus due to component

i of the noted phase. The addition of the slow diffusing element can have a large effect on the

eutectic spacing for a given interfacial velocity.
4.3 Research Accomplishments 33

4.3 Research Accomplishments

This past year we have created three databases: A CALPHAD database for Si-Cr-Co and

Si-Ti-Co alloys, a liquid diffusion database derived from abinitio molecular dynamics, and

a molar volume database for ternary and higher order alloys that is derived from density

functional calculations. The experimental data supplemented with the AIMD calculations

provided parameters to calculate the liquid molar volume of the multi-component Si-CrCo-Ti over a large range of temperatures and compositions. The molar volume database

was employed to mitigate the effects of the expansion of the Si during solidification. Using

this database and the CALPHAD database in a Scheil-Gulliver model for solidification,

alloy compositions were chosen that yield a molar volume of the last-to-solidify liquid

that is similar to that in the solid. Initial calculations in the Si-Cr-Al system matched the

experimental data from Dow Corning, confirming that this approach can be used to design

alloys that minimize solidification induced fracture. In addition, we have developed a

model for two-phase multicomponent eutectic growth. The model can be used to predict,

for example, the dependence of the eutectic spacing on the addition of a ternary alloying

element to a binary alloy. This model uses the the CALPHAD and liquid diffusion

databases. In an effort to develop a more isotropic eutectic morphology, the effects of

small additions of Sr on the morphology of the Si phase in the Si-Cr eutectic was examined.

The results of experiments performed at DCC were quite surprising. Unlike past work,

the Sr had little effect on the morphology of the Si plates in the eutectic. This finding

indicates that Sr alone does not affect the growth of the facetted Si phase. The changes

in morphology that have been reported in the literature must therefore be due to some

other effect, such as the presence of both Al and Sr in the alloy. This past year we also

began work on a phase field crystal model for solidification with the highly anisotropic

solid-liquid interfacial energies. These highly anisotropic interfacial energies can lead to

the facetted microstructures observed in Si alloys.

4.4 Collaborations

4.4.1 NIST Collaborations

CALPHAD Assesment of Si Alloys

Ursula Kattner (NIST), Peter Voorhees(NU)

Voorhees group collaborated with Kattner, an expert on CALPHAD assesment of phase

equilibria, for CALPHAD assesment of Si alloys.

Phase Field Model Development

James Warren (NIST), Peter Voorhees(NU)

Voorhees group collaborated with Warren on model development.

4.4.2 External Collaborations

In-situ Si Composites

Peter Voorhees (NU), Vasgen Shamamian (Dow-Corning), Kwon Skinner (Dow-Corning),

Andres Becerra (Dow-Corning), Lance Wu (Dow-Corning)

Voorhees group closely collaborated with Si-alloys group at Dow-Corning for this project.
34 Chapter 4. In-Situ Silicon Composites

4.5 Publications and Presentations

3

Number of presentations on CHiMaD supported research in 2015, please see section

21.3 for the complete list.

4.6 CHiMaD Team

In-Situ Si Composites

Use-Case Group Researchers

Name Position Affiliation Project (PI)

Peter Voorhees Principal Investigator NU/MSE

Christopher Wolverton Principal Investigator NU/MSE

Nana Ofori-Opoku NIST-CHiMAD Postdoc NU-NIST (Voorhees, Warren)

Oriane Senninger Postdoctoral Researcher NU/MSE (Voorhees)

Shiqiang Hao Postdoctoral Researcher NU/MSE (Wolverton)

Matthew Peters Graduate Student NU/MSE (Voorhees)
5. Directed Self-assembly of Block Copolymer Films for Lithography

Paul Nealey (UC), Juan de Pablo (UC), Steven Sibener (UC), Luping Yu (UC), Heinrich

Jaeger (UC), Ian Foster (UC/ANL)

Significance

Directed self-assembly (DSA) of block copolymers (BCPs) has the potential to revolutionize

nanomanufacturing. BCPs self-assemble to form densely packed features with highly

uniform dimensions and shapes in ordered arrays at the scale of 3 to 50 nm. Chemical prepatterns or templates may be defined using traditional lithographic materials and processes

such as 193 immersion lithography at the scale of 80 nm. By directing the assembly of

block copolymer films on the templates, the overall resolution of the lithographic process

may be increased by three to four-fold or more and still meet the stringent requirements of

manufacturing, including perfection, dimension control, and pattern placement accuracy.

The interest and exponential growth in research activity and expenditure is driven in the

semiconductor industry by the prospect of manufacturing future generations of computer

chips according to Moore’s law, without having to invest billions in new fabrication

facilities (i.e. based on extreme ultra violet lithography) that may or may not be able to

meet the resolution requirements already being demonstrated by DSA. For hard drives,

block copolymer lithography is the only known technology to fabricate nanoimprint masters

to manufacture bit patterned media at the required storage densities greater than 2 Tera

dots/inch2.

5.1 Design Goals

Directed Self-assembly of Block Copolymer Films for Lithography use-case group seeks

to design block copolymer materials and assembly processes to enable manufacturing

via DSA at length scales of 10 nm, scaling to 5 nm, and meet manufacturing constraints

with respect to defects (0.01/cm2), and line edge and width roughness (<1 nm). The

structure of the assembled BCP depends on many variables describing material properties

(molecular weight, block architecture, interaction parameter χ) and process parameters

(temperature, solvent, assembly time, template chemistry and topography, interfacial
36 Chapter 5. Directed Self-assembly of Block Copolymer Films for Lithography

energies, and surface energies); our design objectives can only be met by developing

and validating predicative multi-scale models. Moreover, the immense parameter space

requires evolutionary approaches to simultaneously understand and optimize numerous

variables, in the context of analysis of experimental data and in the design of new materials

and processes. To accomplish our design goals with respect to structure, our priority and

main activity is to advance three-dimensional metrology tools and methods, and develop

expertise in quantitatively comparing predictive models and experimental data. The focus

of former mentioned activities are the independent variables (design criteria) - material

properties, template properties, and processing parameters. Molecular simulation-based

data analysis is the foundation for delineating complex structure-property-processing

relationships and is accomplished by close collaboration with Kline (NIST) and Sunday

(NIST). As we recently reported, the dynamics of structure formation plays a dominant role

in achieving our design goals with respect to minimizing number of defects in assembled

structure. Our predicative models are being refined to include dynamic information,

and new experimental tools based on high-speed AFM are used to quantify processing

pathways.

5.2 Significant Accomplishments in 2015

A key roadblock to the design of materials and processes for sub 10 nm DSA, with scaling

to 5 nm resolution is quantitative three-dimensional metrology, and relating structure to

materials properties, template properties, and processing parameters. At these dimensions,

main-stream metrology provides insufficient insight. A top priority of the user-case group,

undertaken in partnership with NIST, is developing resonant soft x-ray scattering (RSoXS)

as a technique to capture the details of the three-dimensional structure of individual

block copolymer domains, and the collective averages of three-dimensional structure and

pattern perfection over large areas, all as a function of materials, processing, and boundary

conditions used in DSA. Because RSoXS requires BCP samples to be prepared on ultrathin SiN membranes, in this funding period we devised sample preparation techniques for

which all of the DSA processing on sub-10 nm high-χ BCP systems can be completed

on the front side of the wafer prior to fabrication of the membrane by back etching.

We also developed techniques to prepare samples cut from 300 mm wafers supplied

by industrial partners to allow for analysis of the assemblies of highest value. To gain

design-relevant information from the RSoXS experiments, we implemented a strategy

to construct appropriate theoretically informed coarse grained (TICG) models during

data analysis. Independent variables such as BCP molecular weight, composition, and

interaction parameter χ, as well as template properties such as the chemistry and geometry

of guide stripes and background regions are input into the model; simulations predict the

three-dimensional structure which in turn is used to calculate RSoXS spectra. Evolutionary

algorithms find the optimized combination of several independent variables input into

the TICG model that results to the best comparison of the simulated and experimental

data. Validation of the physics-based model and its use in the analysis of the RSoXS

data, collected in reciprocal space, is obtained by transmission electron microscopy (TEM)

tomography. TEM tomography enables direct visualization of a small area of the sample

in real space. Based on the wealth of nanometer scale information in the RSoXS, and the

extent to which this information is probed by our analysis method, we are optimistic that

this MGI approach to predictive model development and validation is unprecedented in

its power to guide the design of next generation DSA systems. Finally, in this funding
5.3 Research Accomplishments 37

period we elucidated the relationship between the thermodynamics and kinetics of DSA

processes and defects. Material and process parameters create a thermodynamic landscape,

and kinetics determine if or for how long defects persist in metastable states at vanishingly

small concentrations. Efforts are now underway to create new models to capture the

dynamics and pathways of assembly, particularly nucleation of structure on the chemical

guiding pattern. Simulation results will be contrasted to data from newly developed high

speed AFM imaging of BCP films during processing.

5.3 Research Accomplishments

Figure 5.1: RSoXS and TEM average cross-sections characterization of P2VP-b-PS-b-P2VP DSA on different guide

stripe widths: (a) 1.5 L0 (b) 0.8 L0. Stripe width of 1.5 L0

led to a shorter P2VP domain on top of the guide stripe; at

0.8 L0 PS appeared on top of the guide stripe while neighboring P2VP domains conformed to the sidewalls of the

guide stripe and tapered at the bottom.

RSoXS of High χ Materials for

Sub-10nm DSA. Over the Year

Two funding period, we were able

to reveal the complex 3D structures in block copolymer DSA,

leveraging the expertise in nanofabrication and 3D characterization developed in the previous

year. Through extensive collaboration with NIST and ANL,

we combined both RSoXS and

TEM tomography to investigate

the materials and processes for the

next generation sub-10 nm DSA.

P2VP-b-PS-b-P2VP, a high χ system that can achieve sub-10 nm

feature sizes, is an important candidate for Generation II DSA materials. P2VP-b-PS-b-P2VP triblock with 10.5 nm half-pitch was

assembled using solvent vapor annealing on back-etched membranes. Samples with a

different guide stripe width appeared aligned from top down views. However, RSoXS

revealed complex 3D structures inside the film that were previously undetected. The

results were confirmed by TEM tomography as presented in Figure 5.1. Characterizing

and understanding these 3D structures is critical to identify the pattern geometries that

would generate through-film domains for optimal pattern transfer.

Material Design Based on Evolutionary Optimization. A key challenge for materials

design is to identify the required components and their structural configuration for given

target properties. In collaboration with the de Pablo group, the Jaeger group introduced a

formalism that can generate optimizers automatically by extending statistical mechanics

into the realm of design. The strength of this new approach lies in its capability to

transform statistical models that describe materials into optimizers to tailor them. This

formalism is broadly applicable, fast, and robust, and it provides a powerful tool for

materials optimization as well as discovery. (see Figure 5.2)

X-ray Data Fitting Based on Molecular Model and Evolution Strategy. Nealey and de

Pablo groups developed a synergistic methodology that links theory-based modeling to

X-ray scattering data and vice versa. The overall scheme is illustrated in Figure 5.3. In
38 Chapter 5. Directed Self-assembly of Block Copolymer Films for Lithography

Figure 5.2: Pattern optimization for directed self-assembly. Optimized pattern parameters for 3x

(A) and 6x (B) density multiplication, including guide stripe width and affinities of the guide and

background regions, are obtained by matching the necessary BCP structure to the target morphology.

Two characteristic configurations before and after optimization, separated by just a handful of

iterations, display markedly different phases of the polymer. Asymptotic configurations depicting

the order parameter (solid, marked line) and the target (dashed line) show that the optimized

parameters match the desired morphology.

this work, experimental data drive an evolution strategy implemented on a finite number of

modeling parameters. The latter then serve as input to molecular simulations that provide

the necessary output. Tailored software then converts output to scattering predictions that

are contrasted to experimental data and the procedure continues iteratively to achieve a

satisfactory description of experimental data. The evolution strategy linked 3D data from

scattering experiment directly to the fundamental modeling parameters. The latter can be

then used for predictive modeling that develops new materials and processes by design.

3D Characterization of Industry-relevant Samples. DSA on chemical pattern using the

"LiNe” flow developed by the Nealey group has been implemented on all-track process at

Imec, a first in the industry. To transfer the 3D characterization techniques to the industry,

we collaborated with Imec to adjust the back etch process and successfully prepared

membrane samples from 300 mm wafers. By not damaging the crucial pattern-polymer

interface, the back etch technique allowed the first experimental investigations of the

3D structures in samples from an industrial setting. This work was highlighted by the

Semiconductor Research Corporation (SRC) as the Best in Patterning Session Award in

TECHCON 2015. (see Figure 5.4)

Investigation of DSA kinetics with in-situ AFM. In the DSA process, the material and

process parameters create a thermodynamic landscape, and kinetics determine pathways

from the initial disordered state to the well-aligned structures. Monitoring the initial

stages of microstructure nucleation therefore informs us of the processing conditions

necessary to prevent the formation of metastable defects. It may be possible to reduce the

number of defects formed at early stages of structure formation such that the kinetically

slow processes of defect annihilation at late stages of processing become less important

in terms of pattern perfection. In situ AFM uniquely allows the non-destructive, local

monitoring of polymer dynamics, now achieving real-time video imaging with a rate of

up to 1 image/second. In the preliminary analysis, we have observed the evolution of
5.3 Research Accomplishments 39

Figure 5.3: Overall scheme of scattering data fitting using evolution strategy. A finite number of

modeling parameters serve as input to molecular simulations that provide the necessary structure.

The real space structure is then converted to scattering predictions that are contrasted to experimental

data and the procedure continues iteratively to achieve a satisfactory description of experimental

data

Figure 5.4: (a) Nealey group successfully prepared membrane samples from 300 mm wafer by

back etching coupons. Average cross section of LiNe flow DSA was obtained by RSoXS (b) and

TEM tomography (c).
40 Chapter 5. Directed Self-assembly of Block Copolymer Films for Lithography

Figure 5.5: Growth and ordering of well-aligned lamellar domains on a chemically-patterned

substrate at 240◦C. The (vertical) well-aligned domains coexist with the (horizontal) non-bulk

morphology.

well-aligned domains alongside non-bulk morphologies. As the film is thermally annealed,

the lamellar domains were observed to grow and become more highly ordered. (see Figure

5.5)

Figure 5.6: Specular (right) and 1st order (left)

diffraction peaks taken on a patterned polymer

sample infiltrated with aluminum oxide at the

IEX beamline. The fact that both the specular

reflection and the first order diffraction peak were

visible proved that the detector has the required

dynamic range for the study.

Commissioning RSoXS beam line at the

APS. Collaborating with Kline (NIST),

CHiMaD funded post doc and student from

the Nealey group performed first scattering experiment at the newly constructed Intermediate Energy X-ray (IEX) beam line,

successfully testing the feasibility of carrying out RSoXS on polymer DSA at the IEX.

On the fly RSoXS data analysis tools was

also implement at the IEX beamline with

the help of the Kline group (NIST). We aim

to greatly improve the efficiency and quality of data collection. Great progress has

also been made in the optimization of the

beam line and testing is expected to finish

in early 2016. (see Figure 5.6)

Polymer Property Data Base. Collaborating with Audus (NIST), de Pablo and Foster developed a semi-automated strategy

for extraction of polymer properties from

the recent literature. The first stage of this

project focused on "chi" parameters for

polymer blends, which are necessary for

design of DSA processes. The contents of

the data base were verified in course cotaught at the University of Chicago by Audus (NIST), Qin (UC), Tchoua (UC) and de

Pablo. The data based now includes more entries for blend chi parameters than available literature compilations. A manuscript describing this semi-automated strategy was

submitted for publication.
5.4 Collaborations 41

5.4 Collaborations

5.4.1 CHiMaD Collaborations

Materials Design for Dynamic Processes

Juan de Pablo (UC), Heinrich Jaeger (UC)

Through this collaboration a new evolutionary algorithm for materials design was developed. This algorithm is approximately two orders of magnitude more efficient (faster)

than existing CMA (Covariance Matrix Algorithm) based strategies (also proposed by our

group), and that can be used to identify dynamic processes leading to target properties in

metastable, out-of-equilibrium systems. The results of that work have been published in

the Proceedings of the National Academy of Sciences [3].

High-Speed AFM Imaging of Block Copolymer Microstructure Nucleation on Chemically

Patterned Substrates

Steven Sibener (UC), Paul Nealey (UC)

The kinetics of initial BCP microstructure formation on patterned DSA substrates is critical

to understanding the origin of metastable defects. High-speed AFM uniquely accesses these

earliest stages, so we may better assess the processing conditions suitable for fabricating

perfected films.

5.4.2 NIST Collaborations

Structure and Dynamics of Entagled Polymeric Nanocomposites

Jack Douglas (NIST), Juan de Pablo (UC)

Through this collaboration a new model for description of the structure and dynamics of

entangled polymeric nanocomposites was developed. The model relies on a slip-spring

formalism to capture entanglement effects on the linear and non-linear rheology of such

systems. A manuscript is being prepared for submission for publication.

Structure of Thin Polymer Films

Jack Douglas (NIST), Steven Sibener (UC)

Douglas and Sibener groups have interacted on graphoepitaxy, dynamics of polymer

structural evolution, and the effect of additives such as nanoparticles on local structure in

thin polymer films.

Physics Based Interpretation of Scattering Data

Joseph Kline (NIST), Daniel Sunday (NIST), Paul Nealey, Juan de Pablo (UC)

Through this collaboration a new formalism that enables interpretation of 3D scattering

data for directed self-assembly of block polymers by relying on a physics-based model

of the material is being developed. The formalism relies on evolutionary strategies to

describe the experimental data, and it is the first of its kind, raising exciting prospects for

development of a new generation of metrology tools. Two manuscripts are being prepared

for submission for publication.

TEM Tomography of Block Copolymers

Jonathan Winterstein (NIST), Alexander Liddel (NIST), Paul Nealey (UC), Juan de Pablo

(UC)

This use-case group collaborated with Dr. Winterstein (NIST) and Dr. Liddel (NIST) to

develop the TEM tomography techniques for 3D visualization of the block copolymer

structures.
42 Chapter 5. Directed Self-assembly of Block Copolymer Films for Lithography

Advanced RSoXS Data Analysis Techniques

Adam Hannon (NIST), Paul Nealey (UC), Juan De Pablo (UC)

Members of the CHiMaD team and the NIST team are actively collaborating in advanced

RSoXS data analysis techniques based on molecular simulations. To this end Adam

Hannon (NIST) spent more than six weeks in Chicago working with the de Pablo group.

Polymer Property Data Base Debra Audus (NIST), Jack Douglas (NIST), Ian Foster (UC),

Juan de Pablo (UC), Ken Kroenlein (NIST)

Members of CHiMaD and NIST collaborate on development of automated tools for data

extraction from the literature and creation of a data base of polymer properties for design

of DSA processes. Audus (NIST) spent a month at the University of Chicago participating

in this collaboration and developing a course on data base creation. As part of this

collaboration, a polymer property predictor tool was developed that includes models for

homopolymers, copolymers, and charged polymers. Tchoua (UC) visited Kroenlein (NIST)

at Boulder to discuss their efforts to extract literature data in the context of thermodynamic

fluid properties for process design.

5.5 Technology Transfer

Software

3D Block Copolymer Simulator

J. de Pablo, P. Nealey, P. Theofanis (INTEL)

Through this collaboration user-friendly versions of the 3D block copolymer simulators

were developed and transferred to the industry. This simulation package is now being used

in a commercial setting at INTEL for development of new DSA materials and processes.

Software

Polymer Property Data Base

D.Audus (NIST), I. Foster (UC), J. de Pablo (UC), R. Tchoua (UC)

Link to beta distribution: http://pppdb.uchicago.edu/ The first stage of this project is

focused on "chi" parameters for polymer blends, which are necessary for design of DSA

processes. The data based now includes more entries for blend chi parameters than

available literature compilations.

Technology

"LiNe" Flow Directed Self-Assembly Method

P. Nealey, R. Gronheid (imec)

DSA on chemical pattern using the "LiNe” flow developed by the Nealey group has been

implemented on all-track process at IMEC, a first in the industry. To transfer the 3D

characterization techniques to the industry, we collaborated with IMEC to adjust the back

etch process and successfully prepared membrane samples from 300 mm wafers. Please

see section 5.3 for more information.

Technology and Know-How

Short-Course on DSA

P. Nealey, J. de Pablo and R. Gronheid A short course on DSA to participants from industry

was taught at the SPIE meeting in San Jose, California, in February 2016. The course was

attended by practicing scientists and engineers from many of the major semiconductor

companies involved in DSA.
5.6 Publications and Presentations 43

5.6 Publications and Presentations

6

Number of CHiMaD-supported publications in 2015 by Directed Self-assembly of

Block Copolymer Films for Lithography use-case group. Please see chapter 20 for

details. [3, 4, 5, 6, 7, 8]

15 Number of presentations on CHiMaD supported research in 2015, please see

section 21.5 for the complete list.

5.7 CHiMaD Team

DSA of Block Copolymers for Lithographic Applications

Use-Case Group Researchers

Name Position Affiliation Project (PI)

Paul Nealey Principal Investigator UC/IME

Juan de Pablo Principal Investigator UC/IME

Steven Sibener Principal Investigator UC/CHM

Luping Yu Principal Investigator UC/CHM

Heinrich Jaeger Principal Investigator UC/PHY

Kieran Murphy Graduate Student UC/PHY (Jaeger)

Gurdaman Khaira Graduate Student UC/IME Evolutionary algorithms for

materials design of DSA (de

Pablo/Nealey)

Brandon Peters Graduate Student UC/IME Directed Self-Assembly of Block

Copolymers (de Pablo)

Alec Bowen Graduate Student UC/IME Directed Self-Assembly of Block

Copolymers (Nealey)

Roselyne Tchoua Graduate Student UC/IME Creation of chi-parameter

Database (Foster)

Jian Qin Graduate Student UC/IME (de Pablo)

Jonathan Raybin Graduate Student UC/CHM Directed Self-Assembly of Block

Copolymer Films for Lithographic Applications (Sibener)

Katherine Oosterbaan Undergraduate Student UC/CHM Directed Self-Assembly of Block

Copolymer Films for Lithographic Applications (Sibener)

Tim Csernica Undergraduate Student UC/CHM Directed Self-Assembly of Block

Copolymer Films for Lithographic Applications (Sibener)

6. Soft Matter Design Based on Charge Complexation

Matthew Tirrell (UC), Juan de Pablo (UC), Erik Luijten (NU), Monica Olvera de la Cruz

(NU)

Significance

Soft matter refers to materials where energies of order kT can influence the structure and

functions of the materials. One of the main inspirations for soft matter design comes

from biology, where many functional materials are built from relatively weak non-covalent

interactions. Considerable success has been achieved in creating new materials based on

hydrogen bonding and van der Waals interactions. In addition these bonding modalities,

and others, biology makes extensive use of electrostatic interactions, frequently mediated

by multi-valent ions, including charged polymers. The Use-Case on Soft Matter Design

based on Charge Complexation is a comprehensive effort to understand, and employ this

understanding to design, new materials based on predictive use of attractive interactions

induced by multi-valent interactions in systems of charged polymers. This Use-Case

comprises experimental and theoretical work measuring, simulating and modeling phase

diagrams in charged polymer systems, micellization and hydrogel formation in charged

block copolymers, and organic inorganic nanocomposites. New materials are being

designed and synthesized for biomaterials, drug and gene delivery, bone and tooth repair.

6.1 Design Goals

The overall design goal of the Soft Matter Design Based on Charge Complexation is to arrive at functional, self-assembled systems for applications in health care. Examples include

micellar nanoparticles, which may be used for controlled drug delivery, bio-imaging, or

gene delivery. In that case the design goal is to develop self-assembled complexes having a

specific size, structure and charge, as well as clearly defined characteristic thermodynamic

properties. Examples also include calcium phosphate nanoparticles, formed by charge

complexation, which may be used for bone replacement therapy or dental repair. In this

case the rheological and mechanical properties of the material are of central importance to

the end use, as well as their compatibility with bone cells. This field is in its infancy, and in
46 Chapter 6. Soft Matter Design Based on Charge Complexation

order to meet these design goals it is necessary to generate the necessary thermodynamic

and rheological data on model systems, it is necessary to develop models capable of describing such data, and it is necessary to develop prototypes, consisting of self-assembled

materials, with which to test some of the central design principles identified and developed

by the use case.

Quantitative Data on Phase Behavior of Complexes Formed by Charged Homopolymer

and Block Copolymers. Tirrell group aims to develop quantitative data on phase behavior

of complexes formed by charged homopolymer and block copolymers. This information

is essential to knowing the properties of the materials formed by complexation such as

viscosity and modulus, which are directly related to phase data such as water content.

Quantitative Prediction of Phase Diagrams in Multi-valent Polyelectrolyte Systems. Olvera

de la Cruz group is developing a self-consistent field approach to the quantitative prediction of phase diagrams in multi-valent polyelectrolyte systems. This work is especially

important in capturing the effects of ion correlations on the phase behavior of polyelectrolyte blends.

De Pablo developed a multi-scale modeling formalism for prediction of the phase behavior

and rheology of coacervates. This work is crucial for understanding the dynamic properties

of charged polymers, including complex coacervates.

Dielectric Effects in Self-assembly. Luijten group has developed a new method for taking

into account dielectric effects in self-assembly, which is being applied to the design of

complexes between synthetic polymers and nucleic acids for the purposes of manipulating

gene expression.

New System Development. De Pablo and Tirrell and co-workers have developed a system

composed of a complex between a synthetic polyelectrolyte and CaP that produces a paste

with useful tunable properties the optimal design of which are currently under investigation.

Data Base and Property Prediction Software De Pablo, in collaboration with Douglas

(NIST) and Audus (NIST), developed a polymer property predictor software that couples

to a polymer property data base and permits calculation of phase diagrams, including for

charged polymers (and coacervates).

New Force Field Development De Pablo group has developed a new atomistic force field

for simulation of calcium phosphate in aqueous solution. The model is being used to

describe the formation of amorphous calcium phosphate nanoparticles.

6.2 Significant Accomplishments in 2015

Towards Predictive Design of Complex-based Materials. Tirrell group have determined the

water content in a series of complex coacervate phases formed from different initial solution

concentrations ranging from 1 to 30%. To very good accuracy, all formed complex phases

with 60% water content, and all exhibited equi- partitioning of salt between supernatant

and condensed phases. If this result hold up to further experimental scrutiny, it will greatly

simplify predictive design of complex-based materials.

Phase Diagrams of Charged Block Copolymers. Tirrell group, in collaboration with

Prabhu (NIST) have expanded our knowledge of the phase diagrams of charged block

copolymers by synthesis and scattering (x-ray and neutron) studies of multiple new block

copolymer samples. Matched sets of di-block and tri-block copolymers behave differently
6.2 Significant Accomplishments in 2015 47

(Figure 6.1). We have shown that while oppositely charged diblock copolyelectrolytes

assemble into micellar assemblies as expected, their triblock analogs prefer to form

interconnected networks and segregate from the bulk of the solution. Next section provides

further explanation.

Coarse-Grained Modeling Tool for Complex Coacervation. De Pablo group has developed

an extended version of its "Theoretically Informed Coarse Grained (TICG)" model for

polymer simulations capable of describing the properties of charged polymers. The tool

is important in that it is built on the same principles as the tools developed for polymer

nano-composites and for DSA, thereby providing a unifying framework with which to

simulate and engineer wide classes of polymer materials. Importantly, this tool can also be

used to predict rheological properties.

Figure 6.1: Scattering intensities from poly (allyl glycidyl ether-b-ethylene glycol) based (A)

diblock and (B) triblock copolyelectrolytes based assemblies at varying total polymer concentrations

in solution. The scattering curves are shifted for the clarity of presentation.

Phase separation in polyelectrolyte blends. Olvera de la Cruz group has identified

two driving forces for phase separation in polyelectrolyte blends: ionic correlations,

described by Γ (in kT), between the charged backbone and counterions, and Flory-Huggins

interactions, described by χ N. Ionic correlations play a particularly important role in

blends with a low concentration of charges, while Flory-Huggins interactions drive phase

separation in blends with high charge concentration. In charge-dilute blends, phase

separation can be driven entirely by ionic correlations to form ionic clusters and pairs.

Analytical generalization of image charges. Luijten group’s simulation approach provides

the first analytical generalization of image charges in decades, extending it to arbitrary

multipoles. By combining this discovery with expansions based upon spherical harmonics,

we developed a method that provides the energies and forces between dielectric objects.

Our method scales linearly with the number of dielectric objects in a system and accelerates

calculations by at least three orders of magnitude compared to the most advanced existing

methods. This opens the way to large-scale scanning of parameter space to explore the

role of dielectric effects.
48 Chapter 6. Soft Matter Design Based on Charge Complexation

Figure 6.2: Representative poly (allyl glycidyl ether-b-ethylene glycol) based A. diblock and B.

triblock copolyelectrolytes.

Figure 6.3: Diverse PEC based self-assembled

structures. (A) Hairy micelles, (B) flower like

micelles and (C) Interconnected PEC aggregates. The water content of the complex domains is a key issue. Schematic not to scale.

Synthesis of oppositely charged amorphous nanocomposite nanoparticles. Collaborative

efforts of the de Pablo and Tirrell groups have been able to prepare oppositely charged

amorphous nanocomposite nanoparticles consisting of amorphous calcium phosphate

nanospheres and an organic polymer. Importantly, we have demonstrated that upon mixing

of these nanoparticles, it is possible to produce injectable pastes with controlled properties

for potential applications in bone and teeth repair.

6.3 Research Accomplishments

Oppositely charged polyelectrolyte block copolymers in aqueous solution. Assemblies of

oppositely charged polyelectrolyte block copolymers in aqueous solutions at low polymer

concentrations have been studied in detail. Oppositely charged polyions complex with

each other, and in process phase separate from the bulk solution. In mixtures of A+B

and A−B diblock copolymers (A+ and A− being the charged blocks, B being the neutral

block, (Figure 6.2A)), however, the neutral block preferentially stays in the bulsolusolution,

leading to micellar assemblies with a polyelectrolyte complex (PEC) cores, the neutral

block forming the corona, and the diblock junction points residing on the core-corona

interface (Figure 6.3A). Scattering from such an assembly would constitute contributions

from the individual micelle scattering (form factor) and from the inter-micelle correlations

(structure factor), with only the latter being dependent on the number concentration of the

micelles. Small angle X-ray scattering (SAXS) measurements on P(EO227−AGE50) based

diblock copolyelectrolyte micelles show a progressively increasing correlation peak with

increasing polymer concentration concomitant with increasing contribution from the intermicelle correlations. At low concentrations (0.5 and 1 wt% total polymer concentration in

solution), the scattering intensity represents the micelles form factor, as is evident from

(Figure 6.3A) for (shown in red and blue curves, respectively). At higher concentration,

the inter-micelle correlations contribute notably to the scattering intensity, and lead to the

appearance of a structure factor peak in 2 wt% and 5 wt% total polymer concentrations

in solution at 0.017 Å

−1

and 0.0235 Å

−1

, respectively (blue and black curves in Figure

6.1A). The inter-micelle distance can roughly be estimated from the structure factor peak

position (d 2π/qmax), and for the 2 wt% and 5 wt% solutions corresponds to 370 Å and

270 Å, respectively.
6.3 Research Accomplishments 49

Nanocomposite nanoparticles for bone and dental repair. Tirrell and de Pablo groups

have produced a new paste material with significant possibilities for applications in bone

and dental repair. The building blocks of the paste, i.e. the hybrid nanoparticles of

controlled size and stability, form spontaneously through a simple process under mild

conditions. We have also found that the characteristic dimensions of the as-synthesized

polymer/amorphous CaP (ACaP) hybrid nanoparticles can be controlled by polymer

molecular weight, as well as the concentration of the polymer and salts in the environment.

Note that a wide array of complementary characterization techniques has been used to

characterize their shape and internal structure. Dynamic light scattering (DLS) was used to

monitor size stability of the nanoparticles and to provide direct evidence for the ability of

the oppositely charged hybrid nanoparticles to self-assemble into cohesive aggregates upon

mixing. Elastic, shear-thinning pastes of tunable elastic modulus were formed by relying

on the electrostatically driven self-assembly of oppositely charged hybrid nanoparticles

of different sizes. The pastes have been engineered to be continuously injected through

medical syringes equipped with needles of different sizes, and can preserve their structure

after injection into an aqueous media. Taken together, these properties render our designer

pastes a simple, inexpensive material that could be applied in minimally invasive bone

repairing surgeries that require injectable formulations.

Figure 6.4: Effect of Γ on the phase diagram of

symmetric polyelectrolyte blends with N = 40,

and charge fraction of 0.2. Hollow circles indicate

the triple point

Melt phase behavior of polyelectrolyte

blends. Olvera de la Cruz group has investigated the melt phase behavior of polyelectrolyte blends with and without salt. Polyelectrolyte blends, where one species is

charged and the other neutral, can serve as

a model system for a mixture of oppositely

charged polyelectrolyte chains, which form

polyelectrolyte complexes (PECs). We

have identified two driving forces for phase

separation in polyelectrolyte blends: ionic

correlations, described by Γ (in kT), between the charged backbone and counterions, and Flory-Huggins interactions, described by χ N. Ionic correlations play a

particularly important role in blends with a

low concentration of charges, while FloryHuggins interactions drive phase separation

in blends with high charge concentration. In charge-dilute blends, phase separation can be

driven entirely by ionic correlations to form ionic clusters and pairs. By a careful selection

of Γ and χN values, we can observe a triple point (Figure 6.4), where the three coexisting

phases differ in ionic structure and ion concentration. From the radial distribution functions

shown in Figure 6.5, it is estimated that the ions are ordered in a liquid-like fashion in

ion-dense phase, form small clusters in ion-intermediate phase, and form pairs in ion-dilute

phase. The addition of salt to the polyelectrolyte blend can induce re-entrant behavior as

the salt concentration is increased.

Melt phase behavior of polyelectrolyte blends. We have extended the scope of charged

block copolymer phase diagrams via the introduction of several new copolymer samples

synthesized during Year Two. As seen in Figure 6.6 below, increasing the length of the
50 Chapter 6. Soft Matter Design Based on Charge Complexation

Figure 6.5: Radial distribution functions of the ions of coexisting phases close to the triple point.

The insets show cartoons of the estimated morphologies. The strong peak of the g(r) of the dilute

phase hints at pair formation. The second oscillation at the intermediate density hints at a significant

fraction of triplets and larger clusters. The oscillations in the g(r) of the dense phase are caused by

a liquid-like ordering of charges

charged end block in tri-block systems such as those in Schematic 6.2B, leads to the

formation of lamellar phases while reducing the endblock length destabilizes ordered

phase formation leading only to disordered suspensions of micelles.

6.4 Collaborations

6.4.1 NIST Collaborations

Coarse-grained Model for Simulation of the Phase Behavior of Polyelectrolytes

Jack Douglas (NIST), Juan de Pablo (UC), Matthew Tirrell (UC)

Through this collaboration a coarse-grained model for simulation of the phase behavior of

polyelectrolytes was developed that includes an explicit description of solvent and ions.

The model is the first to be able to describe complex coacervation in polymeric systems. A

manuscript is being prepared for submission for publication.

Semi-automated Data Base Creation

Debra Audus (NIST), Ian Foster, Juan de Pablo (UC), Matthew Tirrell (UC)

Through this collaboration, we have developed a model for semi-automated data base

creation. The current process now involves a verification step, which was implemented

by teaching an undergraduate course at the University of Chicago during which students

were asked to check the relevance of automated data entries into the data base. Dr. Audus

(NIST) spent three weeks at Chicago collaborating on this project and co-teaching that

course. A manuscript describing this project has been submitted for publication in the

Journal of Chemical Education.

6.4.2 External Collaborations

Hybrid methods for dielectric materials

Erik Luijten (NU), Zhenli Xu (Shanghai Jiao Tong University, China), Shidong Jiang (New

Jersey Institute of Technology)

Prof. Xu and Prof. Jiang are experts on highly efficient boundary integral methods and

dielectric methods. This collaboration aims to combine their innovative ideas with the

Luijten groups new strategies for dealing with dielectric interfaces.
6.5 Publications and Presentations 51

Figure 6.6: Increasing end block length drives system into lamellar phases, while reducing endblock

length destabilizes self-assembled ordered phases.

6.5 Publications and Presentations

6

Number of CHiMaD-supported publications in 2015 by Soft Matter Design Based

on Charge Complexation use-case group. Please see chapter 20 for details. [3, 6, 7,

8, 9, 10]

28 Number of presentations on CHiMaD supported research in 2015, please see

section 21.4 for the complete list.

1

Soft Matter Design Based on Charge Complexation use-case group organized an

international symposium entitled Multivalent Interactions in Polyelectrolytes: New

Physics, Biology and Materials held at Institute of Molecular Engineering, University of

Chicago on October 2-4, 2015.
52 Chapter 6. Soft Matter Design Based on Charge Complexation

6.6 CHiMaD Team

Soft Matter Based on Charge Complexation

Use-Case Group Researchers

Name Position Affiliation Project (PI)

Matthew Tirrell Principal Investigator UC/IME

Juan de Pablo Principal Investigator UC/IME

Erik Luijten Principal Investigator NU/MSE

Monica Olvera de la Cruz Principal Investigator NU/MSE

Samanvaya Srivastava Postdoctoral Researcher UC/IME Predictive design of complexbased materials (Tirrell)

Nader Taheri Qazvini Postdoctoral Researcher UC/IME Hybrid biomaterials (de

Pablo/Tirrell)

Marat Andreev Postdoctoral Researcher UC/IME Biomaterials (de Pablo)

Ashley Guo Graduate Student UC/IME Biomolecule aggregation and detection (de Pablo)

Jiyuan Li Graduate Student UC/IME Biomaterials (de Pablo)

Hadi Ramezani-Dakhel Graduate Student UC/IME Biomaterials (de Pablo)

Ha-Kyung Kwon Graduate Student NU/MSE Biomaterials (Olvera de la Cruz)

Ming Han Graduate Student NU/AP Dielectric Interfaces (Luijten)

Zonghui Wei Graduate Student NU/AP Coarse-grained models for PEI

(Luijten)

Huanxin Wu Graduate Student NU/AP Computational methods for spatially varying permittivity (Luijten)

Jos Zwanikken Assistant Professor UM/PHY Biomaterials (Olvera de la Cruz

- partial funding)
6.6 CHiMaD Team 53

Header Image: Complexation of PEI-PEG graft copolymer with RNA, demonstrating how the double helical structure contracts in the binding process. Models developed

within the CHiMaD use-case provide quantitative predictive capabilities that greatly accelerate the design process

of polymeric carriers for DNA and RNA nanoparticles (Luijten, from Publication [10]).

7. Polymer Matrix Materials

Catherine Brinson (NU), Wei Chen (NU), Juan de Pablo (UC), Sinan Keten (NU), Erik

Luijten (NU)

Significance

While polymer composites are lightweight, corrosion resistant and with tailorable properties, their capabilities are vastly under-utilized due to lack of: 1) understanding of

multiphase composite properties under small constituent changes, 2) ability to tailor

multiple physical properties simultaneously, and 3) durability predictions for long-term

properties under use conditions. Tackling these challenges requires a fundamental understanding of the nature of polymer behavior near interfaces (the interphase) and ability to

predict microstructure and its impact on properties. In this use case group, we pursue an

integrated approach to to relate macroscale polymer composite behavior to chemical constituent and kinetic behavior, and link these resources to development of high-performance

modeling and predictive tools. The research includes experimental investigations mapping

local physical properties, simulations and analysis to predict microstructure and interphase formation, and a data analytics approach to curate, visualize, and develop analysis

and design tools.

The focus of the Polymer Matrix Materials use-case group is initially on polymer nanocomposites, with a long term vision for traditional and hybrid composites. The key of multiphase

polymer response is to understand the interaction of filler particles with the matrix material

and to explore the physical properties of the interphase zone. This is accomplished both by

experimental efforts and physics-based modeling using Molecular Dynamics (MD) and

Monte Carlo (MC) simulations to predict interphase polymer response as a function of confinement, crosslinking density and monomer chemistry. At the same time, the researchers

of the Polymer Matrix Materials use-case are assembling the known data into a central

resource, which will be used with supervised learning methods to identify overarching

trends. Combining the physics based methodology with the curated data and tools will

provide an unprecedented ability to develop new predictive models of composite behavior.
56 Chapter 7. Polymer Matrix Materials

7.1 Design Goals

The ultimate goal for the Polymer Matrix Materials use-case is to develop databases, models

and tools to enable prediction of hierarchical composite behavior based on constituent

components and processing.

In short term this use-case aims to;

• Develop the fundamental understanding of interphase properties in cellulose-polymer

nanocomposites

• Develop databases, models, and tools to manipulate and design interphase properties

in cellulose-polymer nanocomposites and thus control their bulk physical properties

(e.g. diffusion, modulus, Tg)

• Develop design rules to tune properties and control service life (e.g., double interphase modulus and double freeze-thaw cycles)

7.2 Significant Accomplishments in 2015

The polymer matrix materials use case has integrated modeling and experimental work

to understand the origins of the interphase in polymer-particle systems, curated data

on structure and properties, and developed analysis and design tools for nanocomposite

systems. Fundamental research on the interphase has produced local property gradients for

polymers near cellulose surfaces both experimentally and in simulation. The results show

the impact of molecular weight, entanglement and surface chemistry, where significant new

modeling approaches have been validated with significantly improved computational speed.

A data resource has been built, NanoMine, curating data on polymer nanocomposites

from literature and our own data utilizing schemas developed for the Materials Data

Curator system at NIST. Microstructure characterization analysis and characterization

tools have been developed and included in NanoMine. These tools allow reduction of

microstructural images to numerical descriptors and computational reconstruction of

statistically equivalent images in 2 or 3 dimensions which can be used in further simulation

and design simulations. In one application of the data resource, the processing energy

and surface energies for a suite of different polymer-particle systems was analyzed and a

model developed to relate these energies directly to property descriptors. It was shown

that final dispersion is more strongly related to the interfacial energies than the processing

energies. The accomplishments represent both fundamental new understanding in polymer

physics, development of new open-source data and tool resources, and the foundation for

true materials design in this complex arena.

7.3 Research Accomplishments

Data Structures and Databases for Polymers and Composites Polymer composites and

nanocomposites (PNCs) present the distinct challenge of being a system of organic polymer or matrix coupled often times with an inorganic reinforcing filler phase, rendering

such material systems impossible to be thermodynamically simulated from first principle

calculations. A point of emphasis in this project has therefore been to create a centralized

repository of PNC data from many different sources, including published literature, and

a range of data analysis tools that enable quick and efficient mining of the data within

the repository, thus aiding the effective identification of hitherto unknown parameters that

control the structure-property relationships in PNCs and enhance the pace of material
7.3 Research Accomplishments 57

discovery. Such an open-source, centralized data resource, which can be easily accessed

by the research community, is envisioned to encourage collaborative contribution of data

and data-analysis tools developed by different researchers, replacing the currently tedious

and inefficient manual data collection, comparison and analysis efforts performed within

individual research groups. This objective was achieved through the development of a

novel data resource for PNCs called NanoMine (Brinson, Chen, Zhao (NU)) through

customization of the Materials Data Curator (MDC) system developed at NIST with

suitable data structures for experimental and computational PNC data.

The development of NanoMine database involves three distinct tasks: (i) creation of suitable

templates and material data structures ("schemas’") for PNCs, (ii) population/curation of

the database with representative sets of PNC data, and (iii) development and integration of

data-analytics tools and physics-based modeling tools (Brinson, Chen, Li (NU)). Detailed

templates and hierarchical schemas capable of archiving important processing, structure

and property attributes in PNCs have been developed and are being updated on the basis

of an ongoing data curation process. The initial curation efforts, which also contributed

to enabling undergraduate summer outreach (Qu (NU), Schmutz (NU)), were primarily

focused on curating published data from PNCs with surface treated spherical inorganic

nanofillers with explicit reports of nanophase dispersion in micrographs, well-documented

processing and experimental procedure, and functional data of viscoelastic and/or dielectric

properties. More than 500 data sets from experiments and nanocomposite literature have

been manually curated and entered into NanoMine.

Modular statistical learning tools for characterizing material microstructure have also

been developed and integrated with the NanoMine database. The Niblack Binarization

module enables the characterization of the material microstructure from SEM/TEM images,

wherein the individual pixels are assumed to contain background information that is

influenced by and correlated to the neighboring pixels. This approach results in flexible

and enhanced binarization of microstructural features by allowing the user to tune the size

of the pixel neighborhood. Similarly, a descriptor-based three dimensional reconstruction

from a two dimensional image can be achieved using the Descriptor Characterization

module. A third module, Dynamfit, that is currently being integrated into NanoMine

will enable the users to fit a Prony series to viscoelastic master curves, and use the

output functional responses in Finite Element Analysis (FEA) simulations of viscoelastic

properties.

A descriptor-based approach, with two energy descriptors, i.e., processing energy and

interface energy has also been developed to study the processing-structure relationship of

polymer nanocomposites (Brinson, Chen). Supervised learning approaches were applied

on microstructure images, wherein these two processing descriptors were correlated with

the key microstructural descriptor, normalized interface area. The learned processingstructure correlation has been validated on polymer nanocomposites with different combinations of constituents and surface modification methods, which will aid the ongoing

experimental (Brinson, Fox (NIST), Gilman (NIST)) and computational (Keten, Luijten)

efforts aimed at understanding the effects of changes in surface energy (arising from

controlled changes in surface functionalization) of Cellulose Nano Crystal (CNC) filler

particles on the functional properties of PNCs. The complementary Polymerizer simulation

module (Brinson, Chen), developed in collaboration with research groups at Rensselaer

Polytechnic Institute (RPI), has been integrated into NanoMine to enable users to predict

surface energy parameters in nanocomposites, including work of adhesion, wetting angles
58 Chapter 7. Polymer Matrix Materials

Figure 7.1: The results of the all atomistic molecular dynamic simulations have been used in

coarse-grained MD (CG-MD) simulations of model nanocomposite systems to capture the local

changes in glass transition temperatures (Tg) of the interphase regions.

and work of spreading using curated particle surface energies. A FEA-based module

has been developed that incorporates microstructural dispersion and interphase relaxation

phenomena to simulate the dielectric spectroscopic response of PNCs. The prototype

NanoMine website (http://nanomine.northwestern.edu:8000) contains basic functionality

for data curation (data entry with pre-defined schema) and data exploration (selecting

from existing materials and look up data by query). The current beta version is ready for

distribution to volunteering academic and industrial research groups for system testing,

communal curation and feedback. The NanoMine data system is envisioned to interface

with the Polymer Design Database (de Pablo), being developed to computationally predict

structural, thermodynamic and dynamic properties of multiphase functional polymers and

block co-polymers systems and the molecular simulation toolkits being assembled at NIST

(Phelan (NIST), Khare (NIST), Luijten), WebFF and Comsoft Workbench.

Interphase Properties in Polymer Nanocomposites The interphase region between nanoparticles and the matrix polymer, corresponds to a significant volume fraction of PNCs and

thus necessitates an accurate understanding of its properties to accurately predict and tailor

the properties of PNCs. However, direct experimental measurements of the interphase

mechanical and functional properties are scarce in published literature, limiting our understanding of the factors contributing to its development. To this end, direct experimental

characterization of the spatial gradients in elastic modulus and viscoelastic mechanical

properties in polymer thin films and polymer nanocomposites is being achieved through

developments of atomic force microscopy (AFM) based nanoindentation techniques in

the vicinity of interfaces, using both model nanocomposite systems (flat 1-d controlled

systems) and in situ nanocomposites (Brinson, Kolluru (NU), Cheng (NU)). The focus of

these experiments has been two-fold: (a) to provide data that will help validate and provide

the input required for modeling groups (Keten, Luijten, de Pablo) and (b) enable rapid
7.3 Research Accomplishments 59

Figure 7.2: AFM-based nanomechanical indentation experiments (Brinson) on in-situ nanocomposites

progress in understanding the role of variables such as molecular weight, temperature and

strain rate on the interphase mechanical properties, thus bridging the gaps arising from

computational limitations on modeling efforts.

Synergistic experimental and modeling efforts on this front have focused on understanding the mechanical behavior of amorphous polymer matrices reinforced with cellulose

nanocrystals (CNCs) (preferred for the extremely high modulus and renewable nature).

Effects of chemical interactions between the polymer matrix and the filler nanoparticles on

interphase glass transition temperatures have been captured through all atomistic molecular

dynamic (AA-MD) simulations, the results of which have been used in coarse-grained MD

(CG-MD) simulations of model nanocomposite systems to capture the local changes in

glass transition temperatures (Tg) of the interphase regions and these results were subsequently used to predict the Tg changes in bulk-scale nanocomposite systems (Keten), and

are graphically described in Figure 7.1.

Multiscale computational efforts (Luijten) are focused on the development of AA- and

CG-MD models for poly(ethyleneimine) (PEI) based copolymers. These models, applied to

study complexation phenomena, have identified 10-30 fold acceleration in modeling speed,

thus ensuring that the systematic computational design of self-assembled nanoparticles can

be accomplished in reasonable computational time. These computational models provide

two important functions representative of the Materials Genome Initiative: (a) they permit

systematic, reliable and efficient computational exploration, and (b) offer mechanistic

insight into the interactions between PEI and cellulose, enabling systematic design of

additives that alter cellulose surface properties. Internal collaborations are currently

underway to understand and guide experimental surface modification of linear and branched

PEI. External collaborations with Johns Hopkins University are also underway to pursue

the fabrication of PEI-nanoparticles using these models. Complementary experimental

work has focused on the development of CNCs with quantified changes in functional

surface chemistries and energies (Fox (NIST), Gilman (NIST), Davis (NIST), Woodstock

(NIST)), and their incorporation into in-situ nanocomposites with appropriate dispersion

for localized AFM measurements (Brinson, Fox (NIST)). AFM-based nanomechanical

indentation experiments (Brinson) on such in-situ nanocomposites, comprised of unmodified CNCs dispersed in PMMA matrix, have successfully identified single and multilayer CNC dispersions and revealed the local elastic modulus gradients in the interphase

regions around these CNC layers, as shown in Figure 7.2.
60 Chapter 7. Polymer Matrix Materials

Figure 7.3: Studies on model silica-PS nanocomposite system, composed of different monodisperse

molecular weights of PS, have identified that increasing molecular weight leads to a gradual increase in the length scale of the interphase regions

Nanomechanical experiments (Brinson)

also identify the importance of physical interactions/restrictions experienced

by macromolecular chains in the interphase regions of nanocomposites. Studies on model silica-PS nanocomposite

system, composed of different monodisperse molecular weights of PS, have identified that increasing molecular weight

leads to a gradual increase in the length

scale of the interphase region, Figure

7.3, thus suggesting that macromolecular chain length is an important parameter in controlling the development of

an interphase region. Collaborative efforts to computationally (Keten, Luijten)

corroborate the role of macromolecular

chain size are currently underway. An

important outcome of this collaborative

study using low molecular weights polymers will be to provide a perspective on the contribution of molecular weights to the

quantitative and qualitative differences between computational (often limited to low molecular weights) and experimental results. Collaboration between experimental (Brinson)

and computational (Keten) research groups has also focused on ensuring the reliability

of quantitative mechanical property data obtained by way of a sensitive instrumented

indentation methodology. Current experimental efforts are focused on validating recent

computational results describing the effects of AFM-tip radius on the measured interphasial

mechanical properties, Figure 7.4. The studies on reliability and quantitative accuracy are

also being partnered by external collaborations with industrial research partners such as

Dow (Keten) and Goodyear (Brinson, Chen).

Modeling and Materials Design An overarching objective of the Material Genome Initiative is to develop a materials design chart that details the many parameters contributing

to the systematic exploration of the design space and enable the targeted development of

novel materials. Figure 7.5, shows a model system-design chart describing the structureprocessing-properties interaction in the case of PNCs. Critically, composition and processing conditions affect nanoparticle dispersion and interphase size and properties, which in

turn are the major determinants of macroscale response.

Characterization of the dispersion of nanoparticles within the polymer matrix as function

of the different process parameters and material constituents (Brinson, Chen, Keten,

Luijten, de Pablo and Gilman (NIST), Douglas (NIST)) is an important emphasis in

this project. Efforts are underway to apply supervised learning techniques (Chen) on

experimental data (Gilman (NIST)) of carbon nanotube (CNT) dispersion in polymer

matrix materials, to identify important microstructural descriptors that can characterize

CNT dispersion. These descriptors will then be incorporated into MD simulations (Keten,

Luijten) and FEA studies (Brinson, Chen), to identify the eventually achieve optimal

microstructure necessary for targeted properties. It has also been observed that addition of nanoscale particles to polymer solutions can result in complex fluids exhibiting
7.3 Research Accomplishments 61

Figure 7.4: Current experimental efforts are focused on validating recent computational results

describing the effects of AFM-tip radius on the measured interphasial mechanical properties

Figure 7.5: A model systems design chart describing the structure-processing-properties interaction

for PNCs
62 Chapter 7. Polymer Matrix Materials

non-Einstein-like decrease in viscosity, which has not been explained by any available

models. A new particle-based, theoretically informed coarse-grained model (de Pablo) for

entangled nanocomposites has been proposed to predict and analyze the linear response

of PNCs comprised of entangled or unentangled homopolymer matrix. These models

have identified that decrease of the viscosity upon addition of nanoparticles is rooted in

the increase of contributions to the stress relaxation function arising from the particles,

which, for entangled polymers, relax faster than bonded contributions. However, such free

volume effects, which are also present in unentangled polymeric liquids, do not change

the viscosity in that case because the relaxation time for nanoparticle contributions to the

stress relaxation function is comparable to that of bond contributions.

Use of NanoMine and associated data resources provide an important pathway to identify

the parameters that control the properties of the PNCs, and in turn, enable a comprehensive

expansion of the system-design chart presented in Figure 7.5, while other modeling and

experimental studies described in the previous section of this report are aimed at directly

establishing the interphase structure-property relationships that are currently missing in

published literature.

7.4 Collaborations

7.4.1 NIST Collaborations

Characterization of Interphases

Catherine Brinson (NU), Jeffrey Gilman (NIST)

Brinson and Gilman groups collaborated on coordinating AFM and Flourescence experiments in sample preparation and characterization of polymer-particle interactions.

Identification of Key Filler Descriptors for Carbon Nanotubes

Wei Chen (NU), Chelsea Davis (NIST)

In collaboration with Davis (NIST), using carbon nanotubes as an illustrative example,

Chen group will demonstrate the capability of our methods by identifying key filler

descriptors from (CNT) experimental images acquired by Davis (NIST), to achieve a better

understanding of their impact on properties and perhaps correlations with temperature

effects. Statistical characterization and machine learning techniques will be applied to

characterize the filler structures and study their relations with properties.

Characterization of Cellulose Nano-crystals

Wei Chen (NU), Sinan Keten (NU), Jeffrey Gilman (NIST)

This collaboration will characterize the dispersion of cellulose nano-crystals (CNC) and use

MD simulations to predict the impact of key descriptors such as distribution of minimum

distance and compare the results with the experiments of Tg and modules measurements.

Two different CNCs with different surface energies and loading weights (VF) will be

considered.

Coarse-grained Model Development

Sinan Keten (NU), Jack Douglas (NIST), Frederick Phelan (NIST)

This collaboration focuses on coarse-grained model development and implementation in

WebFF.
7.5 Technology Transfer 63

7.4.2 External Collaborations

Nanocomposite Characterization and Modeling

Catherine Brinson (NU), Wei Chen (NU), Linda Schadler (Rensselaer Polytechnic Institute)

This collaboration focuses on fundamentals for understanding polymer nanocomposite

interactions and development of design and analysis tools.

Collaborations with The Dow Chemical Company

Sinan Keten (NU)

This collaboration focuses on determining the reliability and quantitative accuracy of

nanoparticle dispersion and interphase size and properties between computational simulations and experiments.

Collaboration with The Goodyear Tire & Rubber Company

Catherine Brinson (NU), Wei Chen (NU)

This collaboration focuses on determining the reliability and quantitative accuracy of

nanoparticle dispersion and interphase size and properties between computational simulations and experiments.

Collaboration with Dow Corning

Jeffrey Gilman (NIST), Catherine Brinson (NU)

Dow Corning is funding a joint NIST/NU project on quantifying interphase formation in

glass-polymer composites based on results from the Polymer Matrix Materials use-case

group.

7.5 Technology Transfer

Software

NanoMine

R. Zhao, X. Li, C. Brinson, W. Chen, L. Schadler

Link to beta distribution: http://nanomine.northwestern.edu:8000

NanoMine currently can perform basic functionality for data curation (data entry with

pre-defined schema) and data exploration (selecting from existing materials and look up

data by query). The current beta version is ready for distribution to volunteering academic

and industrial research groups for system testing, communal curation and feedback.

7.6 Publications and Presentations

12 Number of CHiMaD-supported publications in 2015 by Polymer Matrix Materials

use-case group. Please see chapter 20 for details. [3, 6, 7, 8, 10, 11, 12, 13, 14,

15, 16, 17]

32 Number of presentations on CHiMaD supported research in 2015, please see

section 21.6 for the complete list.

7.7 CHiMaD Team
64 Chapter 7. Polymer Matrix Materials

Polymer Matrix Materials

Use-Case Group Researchers

Name Position Affiliation Project (PI)

Catherine Brinson Principal Investigator NU/ME

Wei Chen Principal Investigator NU/ME

Juan de Pablo Principal Investigator UC/IME

Sinan Keten Principal Investigator NU/ME

Erik Luijten Principal Investigator NU/MSE

Zhiwei Cui Research Professor NU/ME MD simulations for polymer interfaces (Brinson)

Pavan Kolluru Postdoctoral Researcher NU/ME Local polymer properties via

AFM for Nanocellulose-Polymer

interfaces (Brinson)

Chen Shao Postdoctoral Researcher NU/ME (Keten)

Richard (He) Zhao Graduate Student NU/ME NanoMine (Brinson)

Xiaolin Li Graduate Student NU/ME Pocessing-structure relationship

of polymer nanocomposites,

Nanomine (Brinson, Chen)

Yichi Zhang Graduate Student NU/ME Microstructure characterization

and reconstruction (Chen)

Yongri Su Graduate Student UC/IME Nanocomposites (de Pablo)

Xin Qin Graduate Student NU/ME (Keten)

Wenjie Xia Graduate Student NU/Civil (Keten)

Bobby Sinko Graduate Student NU/ME (Keten)

Zonghui Wei Graduate Student NU/MSE Coarse-grained models for PEI

(Luijten)

Mathias Schmutz Undergraduate Student NU/ME Nanomine data resource curation

(Summer Project) (Brinson)

Karen Qu Undergraduate Stundent NU/ME Nanomine data resource curation

(Summer Project) (Brinson)

Header Image: Regenerated from Figure 7.4 by Xia (NU),

Keten (NU)
8. Precipitation Strengthened Alloys

Gregory Olson (NU), Wei Chen (NU), Christopher Wolverton (NU), Ankit Agrawal (NU),

Alok Choudhary (NU), Yip-Wah Chung (NU), Michael Bedzyk (NU), Wing-Kam Liu

(NU), A. Umantsev (FSU), Jason Sebastian (QuesTek), David Snyder (QuesTek), James

Saal (QuesTek), Ida Berglund (QuesTek)

Significance

New capabilities in predictive design of precipitation-strengthened alloys are being applied

to Co-based superalloys and high-performance Shape Memory Alloys, both employing lowmisfit coherent aluminide precipitates closely analogous to the γ- γ

0 Ni-based superalloys.

The Co-based alloys are of interest for both high-temperature creep and fatigue resistance

in turbine applications, as well as ambient temperature wear-resistance applications such

as aerospace bushings to replace Be-Cu alloys. The shape memory alloys are of high

interest for device and actuation applications in medicine, aerospace and automotive

sectors where ultrahigh-cycle fatigue is the primary limitation of current alloys; here,

efficient precipitation strengthening can eliminate the accommodation slip associated

with thermoelastic martensitic transformation, standing as the principal cause of cyclic

instability.

8.1 Design Goals

Short-term goals for the Co alloys address ambient temperature applications, where the

intrinsic low surface friction characteristics of Co-based alloys makes their precipitation

strengthened variants of clear near-term interest as a replacement for Be-Cu aerospace

bushing applications of current environmental concern. Long-term goals address hightemperature applications (represented by the system chart of Figure 8.1) where metallic

superalloys play a vital role in electrical power generating systems (gas and steam turbines) and jet engines. While achievement of the full set of properties necessary for these

applications is an ambitious goal, the potential of the new family of Co-based superalloys

to achieve the same strength levels of current Ni-based superalloys, but with a temperature

capability 100-150◦C higher would enable a major advance in the fuel efficiency of gas
66 Chapter 8. Precipitation Strengthened Alloys

Figure 8.1: Systems design chart of Cobalt Superalloys

turbines and jet engines. In support of new capabilities in additive manufacturing by 3D

printing, precipitation-strengthened superalloy research will also address process optimization in the 718+ Ni-based alloy being developed under the DARPA Open Manufacturing

program in collaboration with Honeywell.

The breadth of planned databases for Shape Memory alloy design will support both the

near-term optimization of high Pd alloys for medical applications favoring high radiopacity

and low Ni content for biocompatibility (Figure 8.2), and the longer-term design of lowercost high Zr alloys to meet the requirements of aerospace and automotive actuators. This

will allow the new class of cyclically-stable alloys to have broad technological impact.

8.2 Significant Accomplishments in 2015

Experimentation for Co-alloy fundamental data development Alloy prototypes have been

characterized microstructurally via scanning electron microscopy and atom probe tomography. Compression creep testing has been performed for a Co-Al-W-Ti-B alloy for

determination of creep activation energy and stress exponents for elevated test temperatures

up to 900oC. High temperature yield stresses exceed that of commercial polycrystalline

Mar-M247, a Ni-based superalloy.

Co-based CALPHAD Database Development A thermodynamic database for Co-Al-WNi-Ti-Ta system including all the binaries is now deliverable. More complex and accurate

sublattice models were used in the present database compared to other available multicomponent databases. One of the representative accomplishments is the optimization for

the Co-W system, as shown in Figure 8.5 and Figure 8.5.

High-throughput Combinatorial Synthesis High-throughput combinatorial synthesis has

been successfully implemented in the creation of ternary alloys within any section of the

phase diagram. Both the annealing behavior and composition-phase relationship were

examined of films composed of Co, Ti, and Ta using high-flux x-rays and other techniques
8.2 Significant Accomplishments in 2015 67

Figure 8.2: Systems design chart of High-Performance Shape Memory Alloys

at Northwestern and the Advanced Photon Source (APS) at Argonne National Laboratory

(ANL).

DFT Calculation of Anti-Phase Boundary energies and Diffusion in Co Beginning with

accurate benchmark calculations, and based on five-frequency model, accurate diffusion

coefficients for Al and W in Co-alloy were calculated. Reliable experimental data are

available for comparison and it was determined that several vibrational terms can be

accurately approximated or safely ignored. Antiphase boundary (APB) energetics in γ

0

Co3(Al,W)-based superalloys play a critical role in high-temperature strength. Results

indicate a strong dependence of the APB energy on the composition, particularly from the

presence of Al-Al bonds across the 010 APB and W-W bonds across the 111 APB. For

both APBs, calculations also suggest an energetically favorable segregation of Al to the

APB.

High-Performance Shape Memory Alloys Utilizing Fe as an austenite stabilizer, a superelastic Ni-free (Pd,Fe)(Ti,Al) alloy with near-ambient transformation temperatures, low

hysteresis, stable thermal and mechanical cycling behavior, and reversible transformation

strains of 3.2% was designed. A peak-strengthened superelastic Ni-free prototype demonstrated minimal residual strain and excellent cyclic stability during compressive cycling to

3.5% maximum strain.

Fatigue Nucleation Modeling Interactions were explored between a spherical inclusion and

the material’s free surface along with several inclusion microstructures reconstructed from

3D imaging. This work determines the distance at which the free surface interacts with an

inclusion and the effect of applied strain of surface-inclusion interaction. The effects of

inclusion-inclusion interaction, matrix voiding, and matrix strengthening are explored and

ranked with regards to their effect on fatigue nucleation.

Integrating CALPHAD and data mining in design for fatigue performance This year a new

mechanistic model for calculating Ms temperature was investigated based on statistical
68 Chapter 8. Precipitation Strengthened Alloys

analysis on experimental databases. The new model not only gives better estimates of Ms

temperature but also of fatigue strength, which is quite promising and further supports the

sensitivity of Ms temperature for fatigue resistance.

Accelerated Qualification: Accelerated Insertion of Materials (AIM) MethodologyQuesTek

has prepared a second 300-lb. heat of the QT-Co aerospace bushing alloy with the

target nominal composition, including correctcarbon, produced by means of vacuum

induction melting (VIM) followed by vacuum arc remelting (VAR), according to the

standard aerospace practice. Experimental data from ten heats were utilized to correct

the computational material model. The cumulative density function of strength prediction

is obtained based on the bias corrected model to predict the probability of failure. A

comparison study was performed between the proposed Bayesian approach and QuesTek?s

existing Accelerated Insertion of Materials (AIM) approach in predicting the cumulative

density function (CDF) of material properties.

8.3 Research Accomplishments

Experimentation for Co-alloy fundamental data development In collaboration with Lass

(NIST), high-temperature Co-alloys were arc melted and then analyzed for compositional

and microstructural data by Sauza (NU) and Coakley (NU) from Dunand and Seidman

groups. Several alloy prototypes have been characterized microstructurally via scanning

electron microscopy and atom probe tomography. Compression creep testing has been

performed for a Co-Al-W-Ti-B alloy for determination of creep activation energy and stress

exponents for elevated test temperatures up to 900◦C. High temperature yield stresses

exceed that of commercial polycrystalline Mar-M247, a Ni-based superalloy. A Co-Ni-AlMo-Nb alloy prototype, analyzed by Coakley (NU) and Liu (U) (Dunand and Seidman

Groups), demonstrates comparable solvus and creep strength as ternary Co-9Al-9.8W, but

with significantly lower density. Figure 8.3 indicates phase stability of the γ

0 precipitates

in this alloy observed out to 1000 h at 900◦C.

Alloys developed in collaboration with Lass (NIST) included two Co-Ni-Al-Ti-Mo-Nb-Ta

alloys, with one containing W and one without. The alloy containing W was vacuum

induction melted (VIM) and recast as a single crystal by Alcoa-Howmet. The alloy was

further studied during beamtime at Oak Ridge National Lab of in-situ diffraction during

creep. The alloy without W was studied for a unique microstructure consisting of parallel,

directionally coarsened γ

0

. Atom probe tomography was performed, seen in Figure 8.4,

demonstrating Ni, Al, Ti, Mo, Nb, and Ta all partition to the γ

0 phase, while only Co

partitions preferentially to γ.

Co-based CALPHAD Database Development Efforts to establish a multi-component

thermodynamic database (CALPHAD), for the design of Co-based superalloys, continue

to progress through the efforts of Wang (NIST/CHiMaD Postdoctoral Fellow), Xiong (NU)

and Olson (NU). The Co-Al-W-Ni-Ti-Ta system is of primary interest. The Co-Al, Co-W,

Co-Ta, Al-W and Al-Ni systems were reviewed and re-optimized by cooperating density

functional theory (DFT) calculations. The γ

0 phase was described as metastable phase

in Co-Al, Co-W and Al-W systems. A thermodynamic database for Co-Al-W-Ni-Ti-Ta

system including all the binaries is now deliverable. More complex and accurate sublattice

models were used in the present database compared to other available multi-component

databases. One of the representative accomplishments is the optimization for the Co-W

system, as shown in Figure 8.5 and Figure 8.6.
8.3 Research Accomplishments 69

Figure 8.3: SEM micrographs of Co-30Ni-10Al-4Mo-2Nb (at.%) alloy a) homogenized at 1300◦C

for 24 h followed by a water quench. b-f were further aged at 900◦C, followed by water-quenching,

for b) 50 h, c) 100 h, d) 200 h, e) 400 h and f) 1000 h.

High-throughput Combinatorial Synthesis High-throughput combinatorial synthesis, led

by Chung and Bedzyk, has been successfully implemented in the creation of ternary

alloys within any section of the phase diagram. This was achieved by combining DC

magnetron sputtering with photolithography to create a two-dimensional array of points

covering a selected composition range. Both the annealing behavior and compositionphase relationship were examined of films composed of Co, Ti, and Ta using high-flux

x-rays and other techniques at Northwestern and the Advanced Photon Source (APS) at

Argonne National Laboratory (ANL). Single crystal sapphire substrates were used in this

investigation due to their distinct XRD peaks for ease of filtering and thermal stability -

there is no observable substrate-film interaction even after extended annealing at 800◦C.

Because of the refractory nature of component elements, the diffusion kinetics are expected

to be relatively slow. At annealing temperatures of 600-700◦C, metastable phases are

formed after 3-6 hours. Annealing at 800◦C for six hours appears sufficient to yield stable,

homogenized phases as shown in Figure 8.7.

Figure 8.8 shows three sample XRD patterns from alloys with different compositions

after extended annealing at 800◦C. Subtle changes in the diffraction pattern are observed

as a function of composition, indicating evolution of phases. To establish reliability

of this approach to alloy synthesis, this method was used to prepare Co-Ti-Ta singlecomponent and binary alloys previously reported. In addition to verifying agreements with

established databases, our studies reveal the formation of metastable phases not shown in

standard binary phase diagrams when annealed at shorter times. The project has received

commendation for the experimental design from ANL-APS.

DFT Calculation of Anti-Phase Boundary energies and Diffusion in Co Naghavi (NU)

and Wolverton focused on: energetic calculations of binary Co-alloys and compounds,
70 Chapter 8. Precipitation Strengthened Alloys

Figure 8.4: Characterization of Co-10Ni-7Al-4Ti-5Mo-1Nb-1Ta (at.%) alloy aged for 24 h at 900◦C

a) local electrode atom probe (LEAP) reconstruction containing γ

0 precipitate and γ channels, delineated by a 72% Co isosurface b) SEM micrograph (courtesy of Lass (NIST)) demonstrating

elongated precipitate morphology c) proximity histogram demonstrating various elemental concentrations as a function of distance from the γ/γ

0

interface d) magnified portion of (c) to demonstrate

the partitioning of Nb and Ta preferentially to γ

0

.
8.3 Research Accomplishments 71

Figure 8.5: Calculated CALPHAD enthalpy of formation of the μ phase compared to first-principles

(FLAPW) results. The reference state is Hcp Co (non-magnetic) and Bcc W. The open blue circles

are the calculated enthalpy of formation for all the end-members of μ phase. The composition of

each end-member is presented by a five-digit number like 22122, where 1 corresponds to Co and 2

to W, and the order of digits reflect the occupancy of the corresponding sublattices in the following

order: Co1(3a)-W1(6c1)-W2(6c2)-W3(6c3)-Co2(18h). For example, 12221 corresponds to the

nominal μ-phase Co7W, 22221 - the "new’" μ-phase Co6W7. The error bar for the FLAPW results

is ±3kJ/mole-atoms (or 0.03 eV/mole-atoms).

Figure 8.6: Calculated Co-W phase diagram compared with experimental data
72 Chapter 8. Precipitation Strengthened Alloys

Figure 8.7: Evolution in the

x-ray diffraction pattern of

Co81Ti6Ta13 as a function of annealing. The sample was first annealed for 3 hours at 600◦C, followed by an annealing at 800◦C

for (a) 2 hours, (b) 4 hours, (c)

6 hours, (d) 9 hours. The Final

two anneals(c), (d), did not bring

about significant changes in the

observed peaks. The peaks were

baseline-subtracted and fitted using IgorPro

Figure 8.8: X-ray diffraction

patterns from three different

Co-Ti-Ta alloys annealed

at 650◦C for 9 hours: (a)

Co80Ti6Ta14, (b)Co71Ti5Ta24,

(c)Co61Ti10Ta29, and (d) the

2D array of Co-Ti-Ta ternary

alloy samples. The peaks were

linear-baseline subtracted in

IgorPro
8.3 Research Accomplishments 73

diffusion process in Co alloys, and Anti-phase boundary (APB) calculations of Co3(Al,W)

compounds. The goal in energetic calculations of binary Co-alloys is to build a database

of solute-vacancy binding energies in fcc-cobalt based on accurate first-principles density

functional calculations. To do so, the solute-vacancy binding energy, dilute mixing energy,

dilute volume of mixing, and solubility enthalpy of 27 substitutional solutes in fcc-Co are

calculated using Density Functional Theory. Including all of the alloying elements used in

Co-based superalloys which cover most 1st-3rd transition metal elements - except groups 3

and 12- together with Si, C, B and Al, it was discovered that energetic parameters such

as solute-vacancy binding energy vary systematically across the transition metals. This

behavior is explained by a band-filling effect, known as Friedel’s model. The observed

trend can aid experimentalists by estimating energetic parameters of transition metals prior

to making any measurements or calculations.

In regards to diffusion, the ultimate goal is to build a diffusion coefficient database of

transition metals in cobalt. However, this is a difficult (probably impossible) task due to

the high computational costs of an exact diffusion calculation. Simplified methods are

required. To simplify such calculation, key terms must be approximated in the diffusion

model without losing much accuracy. Beginning with accurate benchmark calculations

to analyze each term carefully, and based on five-frequency model, accurate diffusion

coefficients for Al and W in Co-alloy were calculated. Reliable experimental data are

available for comparison and it was determined that several vibrational terms can be

accurately approximated or safely ignored.

Antiphase boundary (APB) energetics in γ

0 Co3(Al,W)-based superalloys play a critical

role in high-temperature strength. APBs can affect the magnitude and temperature-range of

the yield strength anomaly. APBs in L12 Co3(Al,W) γ

0

are particularly complex due to the

presence of the metal mixing on the Al/W sublattice, which introduces additional degrees

of freedom to APB behavior, such as Al/W composition and configuration local to the APB.

The behavior of APBs in L12 Co3(Al,W) were explored with density functional theory.

Results indicate a strong dependence of the APB energy on the composition, particularly

from the presence of Al-Al bonds across the 010 APB and W-W bonds across the 111

APB. For both APBs, calculations also suggest an energetically favorable segregation of

Al to the APB. Therefore, future Co alloy design criteria can include alloying elements

with preferential segregation to the APB over that of Al to tune the APB energy (and the

behavior of the yield strength anomaly). Furthermore, the unique behavior of Al-Al and

W-W bonds in the 010 and 111 APB’s, respectively, indicate the possibility of preferentially

modifying one APB over another with alloying.

High-Performance Shape Memory Alloys Frankel (NU), supervised by Olson, focused

on the development of biocompatible, precipitation-strengthened fatigue resistant PdTibased SMAs for biomedical applications (as representedin the system chart of Figure 8.2).

Controlled precipitation of nanoscale, low-misfit, L21 Heusler aluminides can provide

effective strengthening. Phase relations, precipitation kinetics, transformation temperature,

transformation strain, cyclic stability, and mechanical properties were characterized in

both Ni-free (Pd,Fe)(Ti,Al) and low-Ni high-strength hybrid (Pd,Ni)(Ti,Zr,Al) systems.

Atom probe tomography was employed to measure phase compositions and particle sizes

used to calibrate LSW models for coarsening kinetics and Gibbs-Thompson models for

composition trajectories for systems under evolving unstable equilibrium. Mechanical

and thermal cyclic stability were investigated using compression testing and differential

scanning calorimetry. Mechanical properties were characterized using room temperature
74 Chapter 8. Precipitation Strengthened Alloys

and high temperature Vickers microhardness as well as nanoindentation.

Figure 8.9: Stress-strain response from compressive fatigue

testing performed on a Ni-free Pd42.2Fe7.8Ti44Al6 alloy at

T=Af +25◦C.

Utilizing Fe as an austenite stabilizer, a superelastic Ni-free

(Pd,Fe)(Ti,Al) alloy with nearambient transformation temperatures, low hysteresis, stable thermal and mechanical cycling behavior, and reversible transformation strains of 3.2% was designed. A peak-strengthened

superelastic Ni-free prototype

demonstrated minimal residual

strain and excellent cyclic stability during compressive cycling

to 3.5% maximum strain (Figure

8.9). Due to Pd softening, the

addition of Zr was considered to

improve strength in a low-Ni hybrid (Pd,Ni)(Ti,Zr,Al) alloy. Aging studies at 600◦C resulted in

unusually fast coarsening kinetics, while low-temperature aging studies at 500-530◦C revealed the presence of a third

Zr-rich phase in association with the matrix and Heusler phase. A strengthening study on a

non-transforming hybrid prototype showed lower than expected precipitation strengthening

at 600◦C but significant strengthening when aged at 500◦C due to high volume fractions of

the Zr-rich phase. Transformation temperatures, transformation strain, and cyclic stability

were characterized in a set of transforming hybrid prototypes.

Fatigue Nucleation Modeling The potency of nucleating inclusions is an important factor

in the fatigue life of shape memory alloys. For other alloy systems, computational models

have proven an effective means of determining the relationship between inclusion features

and fatigue life. Moore (NU) under the supervision of Liu extended a subset of those

models, based on crystal plasticity, to examine the role inclusion microstructure plays

in the fatigue life of NiTi alloys using a computational model based on crystal plasticity.

We explore the interaction between a spherical inclusion and the material’s free surface

along with several inclusion microstructures reconstructed from 3D imaging. This work

determines the distance at which the free surface interacts with an inclusion and the

effect of applied strain of surface-inclusion interaction. The effects of inclusion-inclusion

interaction, matrix voiding, and matrix strengthening are explored and ranked with regards

to their effect on fatigue nucleation.

Three meshes were created based on the mesh shown in the Figure 8.10. These meshes

modeled two inclusions with a void, two inclusions without a void and one inclusion (the

largest) without a void. Preliminary results show a three to ten times increase in fatigue

life when voiding does not occur. This confirms that it is the debonding and voiding that is

increasing the fatigue crack nucleation potency around typical inclusion stringers rather

than mechanical interaction. Thus, process optimization to reduce voiding and debonding

will increase fatigue performance, and stringers formed from fracturing inclusions will be

more detrimental than naturally occurring clusters of unfractured inclusions. However,
8.3 Research Accomplishments 75

the increase is fatigue life found from removing voiding is still small as compared to

the fatigue life improvement caused by increasing NiTi matrix yield strength by 50%.

Moore (NU) defended his doctoral thesis in 2015 and a manuscript has been submitted for

publication.

Figure 8.10: Representative mesh for

modeling two inclusions

At length scales where dislocation plasticity becomes size dependent, nonlocal mechanics models

can provide greater simulation accuracy. Umantsev has collaborated with QuesTek in assessing

prior literature for adaptation of the Phase-Field

Method to the micromechanics of fatigue.

Integrating CALPHAD and data mining in design for fatigue performance A collaboration of

Agrawal, Choudhary, Olson, and Xiong (NU),

aims to design an effective interface between data

mining techniques and CALPHAD methodology

for materials genomic design. The research studies

are performed on the carbon steels fatigue strength

data with raw data sets obtained from experiments

provided by NIMS (National Institute for Materials Science, Japan). This year a new mechanistic

model for calculating Ms temperature was investigated based on statistical analysis on experimental databases. The new model not only

gives better estimates of Ms temperature but also of fatigue strength, which is quite promising and further supports the sensitivity of Ms temperature for fatigue resistance. Building

on the success of our CHiMaD demonstration of the integration of data analytics with CALPHAD knowledge, QuesTek has won a $3.6 million DARPA-SIMPLEX multi-institutional

project with an NU team based in CHiMaD, applying this approach in the computational

design of thermoelectric materials.

Accelerated Qualification: Co-alloy Process Optimization QuesTek (Olson, Sebastian,

Snyder, Saal, Berglund) successfully cast a 300-lb. heat of QT-Co with the target nominal

composition, including carbon, produced by means of vacuum induction melting (VIM)

followed by vacuum arc remelting (VAR), according to the standard aerospace practice

(Figure 8.11). Following delivery of the as-cast ingot, a thorough homogenization and

hot working optimization was executed - including heat treatment validation studies and

Gleeble thermo-mechanical simulations. Using light-optical microscopy and electron

microprobe analysis (EMPA), the as-VAR microsegregation was analyzed for dendrite arm

spacing and chemical inhomogeneity. The as-cast analysis is used to calibrate DICTRA

simulations to determine an optimal homogenization procedure.

Modeling activities at QuesTek have centered on achieving higher yield strength in QT-Co

through simulation-driven thermal process optimization. In particular, homogenization

treatment optimization has addressed more efficient homogenization and a reduction in

the anneal time. To reduce the isothermal temper time of 72 hours to peak strength, an

experimental aging study was coupled with DICTRA solidification and homogenization

simulations to identify novel temper schemes, including a two-step process. The 1050◦C

homogenization scheme has been verified experimentally, providing adequate dissolution

of the as-cast dendritic structure. These CALPHAD simulations employed previously

optimized thermodynamic and kinetic databases from Year One activities. With the correct
76 Chapter 8. Precipitation Strengthened Alloys

carbon content established in this second heat, the designed TiC-based grain refinement

should allow achievement of the yield strength goal.

Figure 8.11: As cast 300-lb VIM/VAR heat of QT-Co

QuesTek has obtained an Air

Force SBIR contract to aid the

qualification of the QT-Co bushing alloy, and has submitted a proposal to the Environmental Security Technology Certification Program (ESTCP) for further qualification testing. A proposal has also

been submitted to the National Institute of Health (NIH) for validation testing of the PdTi-based

shape memory alloy for medical

applications.

Accelerated Qualification: Accelerated Insertion of Materials (AIM) Methodology Collaboration continues between QuesTek and Chen. Several research tasks have been completed,

including the development of a powerful metamodeling approach based on the Gaussian

Process Regression (GPR). GPR can replace the time-consuming computational material

models currently in use, such as the process-structure-property simulations of QuesTek’s

Ferrium M54 steel. The metamodel uses input variables that include chemical compositions and processing temperature profile while the output is the prediction of material

properties such as the ultimate tensile strength. Secondly, a Bayesian model bias correction

approach was employed to integrate information from both material simulation models

and the experimental data in predicting the probability that material strength is smaller

than a critical threshold. Experimental data from ten heats were utilized to correct the

computational material model. The cumulative density function of strength prediction is

obtained based on the bias corrected model to predict the probability of failure. Figure

8.12 shows a comparison study between the proposed Bayesian approach and QuesTek’s

existing Accelerated Insertion of Materials (AIM) approach in predicting the cumulative

density function (CDF) of material properties. Our study illustrates the advantage of the

Bayesian approach with its capability of providing the 95% confidence bounds of CDF

curves by taking into account the epistemic uncertainty due to the lack of experimental

and simulation data while the AIM approach provides the prediction of the CDF profile

as shown in Figure 8.12 without uncertainty quantification. This research provides an approach that can quantitatively assess a material property’s probabilistic behavior by taking

into account the epistemic uncertainty due to the lack of experimental and simulation data.

The work is significant in enhancing the confidence of using the computational materials

design approach, with ultimate application in qualification of the new Co-alloys.

8.4 Collaborations

8.4.1 CHiMaD Collaborations

Scalable Frameworks for Mesoscale Modelling

David Dunand (NU), David Seidman (NU), Olle Heinonen (ANL)

Discussions were initiated with Heinonen and Josikaari (ANL), on the subject of adapting

their scalable framework for mesoscale modeling of microstructural evolution to the
8.4 Collaborations 77

Figure 8.12: Bayesian approach vs. AIM approach in predicting the probability that the material

strength is lower than 280 psi

problem of γ

0

-strengthened Co-base superalloys, with the goal of modeling the coarsening

and rafting behavior of the strengthening γ

0

to inform future alloy and process design.

High-throughput DFT Methods for Alloy Design

David Dunand (NU), David Seidman (NU), Christopher Wolverton (NU)

Discussions with Wolverton, Naghavi (NU), and Hegde (NU) to refine properties/areas of

interest where high-throughput DFT methods might inform future experimentation/alloy

design or vice versa. Students and postdoctoral researchers from both groups attended

the fall materials design workshop, where they collaborated on a system design chart for

γ

0

-strengthened high-temperature Co alloys.

Nucleation: Parallel Computing using ANL’s Supercomputer ’Blues’

Alexander Umantsev (FSU), Olle Heinonen (ANL)

Due to the nature of rare-event processes, simulation of nucleation requires long computational time. This brings up a need in a fast computing facility. ANL’s "Blues"

Supercomputer was utilized for the project.

8.4.2 NIST Collaborations

Phase stability of Co-Alloys

Christopher Wolverton (NU), Ursula Kattner (NIST), Carelyn Campbell (NIST)

There is a strong communication between the Wolverton group with Kattner (NIST) and

Campbell (NIST).

Co-alloy Microanalytical Characterization

David Dunand (NU), David Seidman (NU), Eric Lass (NIST), Kil-Won Moon (NIST),

Maureen Williams (NIST)

The NIST experimental group led by Lass (NIST) arc-melted a series of prototype Co

alloys, in which we are collaborating on further evaluation. Information obtained from

these alloys (e.g. γ

0

stability, composition, size, phase fraction) will serve as the basis for
78 Chapter 8. Precipitation Strengthened Alloys

the next iteration of alloy design. Regular teleconferences occur between groups to update

on progress and discuss next steps.

8.4.3 External Collaborations

AIM Methodology

Wei Chen (NU), David Snyder (QT), Jiadong Gong (Questek Innovations)

Chen group collaborated with Snyder (QT) and Gong (QT) to retrieve both simulation

and experimental data for the supper alloy. Chen group analyzed the ten heats data with

21 lots and extracted the important time-history thermal information for each experimental

data.

8.5 Publications and Presentations

16 Number of CHiMaD-supported publications in 2015 by Precipitation-strengthenedAlloys use-case group. Please see chapter 20 for details. [18, 19, 20, 21, 22, 23,

24, 25, 26, 27, 28, 29, 30, 31, 32, 33]

32 Number of presentations on CHiMaD supported research in 2015, please see

section 21.7 for the complete list.

Header Image: Scanning electron micrograph depicting

cuboidal (L12) γ

0

-precipitates surrounded by channels of

(f.c.c.) γ-matrix in an aged Co-Al-W-Ti-B alloy (D. Sauza,

D. Dunand, D.Seidman).

8.6 CHiMaD Team
8.6 CHiMaD Team 79

Precipitation-Strengthened Alloys

Use-Case Group Researchers

Name Position Affiliation Project (PI)

Gregory Olson Principal Investigator NU/MSE

Wei Chen Principal Investigator NU/ME

Christopher Wolverton Principal Investigator NU/MSE

Ankit Agrawal Principal Investigator NU/EECS

Alok Choudhary Principal Investigator NU/EECS

Yip-Wah Chung Principal Investigator NU/MSE

Michael Bedzyk Principal Investigator NU/MSE

Wing Kam Liu Principal Investigator NU/ME

Alexander Umantsev Principal Investigator FSU/PHY

Jason Sebastian Principal Investigator QT

David Snyder Principal Investigator QT

James Saal Principal Investigator QT

Ida Berglund Principal Investigator QT

Peisheng Wang NIST/CHiMaD Postdoc NU/MSE Co-Alloy database development (Chen)

Wei Xiong Research Associate NU/MSE materials design (Olson)

Zequn Wang Postdoctoral Researcher NU/ME (Chen)

Shahab Naghavi Postdoctoral Researcher NU/MSE (Wolverton)

James A. Coakley Postdoctoral Researcher NU/MSE Co-alloys (Seidman/Dunand - EU Marie

Curie Fellow)

Daniel Sauza Graduate Student NU/MSE Co-Alloys

(Dunand/Seidman)

Dana Frankel Graduate Student NU/MSE Shape Memory Alloys (Olson - Graduated Ph. D.)

John Moore Graduate Student NU/ME Fatigue (Liu - Graduated

Ph. D.)

Qinyuan Liu Graduate Student NU/MSE Fatigue (Dunand/Seidman -

Left CHiMaD program)

Cameron Gross Graduate Student NU/MSE High Throughput Combinatorial Synthesis and Analysis of Ternary Alloys

(Chung/Bedzyk)

Logan Ward Graduate Student NU/MSE Data Mining (Wolverton)

Janak Thapa Graduate Student NU/MSE High Throughput Combinatorial Synthesis and Analysis of Ternary Alloys

(Chung/Bedzyk)

Chuan Liu Graduate Student NU/MSE SMA for biomedical applications (Olson)

Orion Kafka Graduate Student NU/ME Fatigue (Liu)

Ding-Wen (Tony)

Chung

Graduate Student NU/MSE Co-alloys

(Dunand/Seidman)

Nathaniel Murry Undergraduate Student FSU/PHY Additive Manufacturing

(Umantsev)

Shardai Johnson Undergraduate Student FSU/PHY Additive Manufacturing

(Umantsev)

Furaha Ive Undergraduate Student FSU/PHY Nucleation (Umantsev)

9. Organic Bulk Heterojunction Polymer Solar Cells

Luping Yu (UC), Juan de Pablo (UC), Guilia Galli (UC), Tobin Marks (NU)

Significance

Bulk heterojunction organic solar cells (OSCs) represent an alternative solar energy

harvesting system, which is pursued by numerous research groups world-wide. Commercial

products are starting to appear in the market from several companies. This use-case

group is addressing critical issues that affect the performance of OSC devices through

materials design and synthesis to test new structure/function ideas, exploring new means

of morphological control and its influence on OSC performance, new approaches to device

optimization, data collection/analysis on new formulations and device characteristics,

and on structure/property relationships. New electron donors or acceptors exhibiting

superior photovoltaic properties are being developed, and their synergistic interactions

are being studied. General rules for designing new polymers for use as charge relays

or energy harvesting antennae in ternary OSCs are also being investigated. In close

coupling to these experimental efforts, new theoretical methods are being developed to

elucidate key electronic processes (electronic excitation, charge separation and transport)

in our champion polymer systems and ternary polymer solar cells. To this end, large-scale

modeling approaches are also being developed to simulate the morphology formation,

polymer chain aggregation, and ways to control them. These studies are also closely

coupled with efforts by NIST scientists to fully utilize their expertise in structural and

morphological characterization of CHiMAD materials. These new materials and the basic

knowledge gained will deepen our understanding of structure/property relationships and

accelerate the development of OSCs towards more efficient and cost-effective commercial

products.

9.1 Design Goals

The main design goal of the Organic Bulk Heterojunction Polymer Solar Cells use case

group is to arrive at all-organic solar cells with reproducible efficiencies that are well above

those of currently known for organic thiophene-based materials (ca. 8%). In long term, a
82 Chapter 9. Organic Bulk Heterojunction Polymer Solar Cells

key aim of this group is to arrive at all-polymer organic photovoltaic materials that will

obviate the need for fullerenes. The development of OPV materials to date has largely

followed an Edisonian approach. This group seeks to identify key molecular and structural

descriptors of performance which may be used in the future to rationally design improved

materials for next generation OPV cells. To that end, the group is targeting the following

activities:

• Develop novel ternary systems that can significantly enhance the power conversion

efficiency (PCE) values of devices. Systematically survey the structures of third component polymers to establish structure/property relationship on energy level matching,

template effects for self-assembly, and the relationship between composition and Voc

values.

• Develop new polymers with varied band gaps, energy levels and optimized charge

transport behaviors for use as electron donor in OPV solar cells. Detailed structure

property relationships are being sought.

• Develop efficient all organic, non-fullerene electron acceptors to replace fullerenes.

• New methods are being developed to create predictive atomistic force fields to fully

describe the aromatic semiconducting polymers. These new force fields are being

used in conjunction with advanced sampling simulation techniques to predict the

structure of OPV polymeric materials in a variety of processing-relevant environments.

• The electronic properties of these polymers are modeled based on sophisticated

classical and quantum mechanical calculations in order to understand performance

from first-principles considerations.

9.2 Significant Accomplishments in 2015

Ternary Blend Organic Solar Cells Yu developed efficient ternary blend OSC with a

PCE of 9.2% was developed (Figure 9.2). Their efforts show that the third component

can reduce surface trap densities in the ternary blend. Detailed studies unravel that the

improved performance results from synergistic effects of enlarged open circuit voltage,

suppressed trap-assisted recombination, enhanced light absorption, increased hole extraction, efficient energy transfer and better morphology. The working mechanism and high

device performance demonstrate new insights and design guidelines for high performance

ternary blend solar cells and suggest that ternary structure is a promising platform to boost

the efficiency of OSCs.

Design of Building Blocks for Organic Semiconductor Materials Marks developed a new

building block, bithiphenesulfoamide, as a new photovoltaic materials component. It was

designed and developed to offer a strongly electron-withdrawing and strongly dipolar

sulfonamide group. This key building blocks allow systematic synthesis of a series of new

donor polymers for further exploration of OPV properties.

DFT of Donor Polymers Galli and Goldey (UC) used B3LYP density functional techniques

to examine the structures and properties of the donor polymers poly-3-oxothieno[3,4-

d]isothiazole-1,1-dioxide/ benzo-dithiophene (PID2) and polythieno[3,4-b]-thiophene/

benzodithiophene (PTB7) and the acceptor material [6,6]-phenyl C71 butyric acid methyl

ester (PC71BM). Energy levels match experimental cyclic voltammetry data well, with

errors of 0.1 eV. The experimental energy levels for these materials reflect the lowest

energy geometries that were calculated.
9.3 Research Accomplishments 83

Figure 9.1: Structures of new wide band gap polymers

Molecular Simulations of OPV-revelant Materials De Pablo carried out molecular simulation of OPV-relevant materials and investigated the ring stacking behavior of single PTB7

molecules with and without its sidechains, and that of PTB7 in the bulk. It was found

that single PTB7 molecules without sidechains show highly coiled configurations with

an average 70% of the rings stacked for 50-mers, while steric hindrance in the full PTB7

molecule (with sidechains) reduces this value to 30%. Improved solvation in the a bulk

material of PTB7, as compared to PTB7 in the vacuum, increases end-to-end distance by a

factor of 3 (for 50-mers) and increases stacking to 70%.

9.3 Research Accomplishments

Development of Novel Photovoltaic Polymers A new series of wide band gap photovoltaic

polymers based on the N-alkyl 2-pyridone dithiophene (PDT) unit was developed (Figure

9.1) by Yu group. These polymers are effective bulk heterojunction solar cell materials

when blended with phenyl-C71-butyric acid methyl ester (PC71BM). They achieve power

conversion efficiencies (up to 5.33%), a high values for polymers having such large

bandgaps, ca. 2.0 eV (optical) and 2.5 eV (electrochemical). Grazing incidence wide-angle

X-ray scattering (GIWAXS) reveals strong correlations between p-p stacking distance

and regularity, polymer backbone planarity, optical absorption maximum energy, and

photovoltaic efficiency. These polymers exhibit potential as third components in ternary

polymer solar cells.

Ternary Blend Organic Solar Cells The integration of multiple materials with complementary absorptions into a single junction device is regarded as an efficient way to enhance

the PCE of organic solar cells. However, because of increased complexity with one more

component, only limited high-performance ternary systems have been demonstrated previously. An efficient ternary blend OSC with a PCE of 9.2% was developed (Figure 9.2)

by Yu group. We show that the third component can reduce surface trap densities in

the ternary blend. Detailed studies unravel that the improved performance results from

synergistic effects of enlarged open circuit voltage, suppressed trap-assisted recombination,

enhanced light absorption, increased hole extraction, efficient energy transfer and better

morphology. The working mechanism and high device performance demonstrate new

insights and design guidelines for high performance ternary blend solar cells and suggest

that ternary structure is a promising platform to boost the efficiency of OSCs.
84 Chapter 9. Organic Bulk Heterojunction Polymer Solar Cells

Figure 9.2: Molecular structures of PID2, PTB7-Th and PC71BM, and Energy levels of materials

used in the solar cell device. (a) J-V curves of ternary solar cells (PTB7-Th:PID2:PC71BM) with

different PTB7-Th:PID2 weight ratios. (b) External quantum efficiencies of ternary solar cells

(PTB7-Th:PID2:PC71BM) with different PTB7-Th:PID2 weight ratios.

Design of Building Blocks for Organic Semiconductor Materials Bithiphenesulfoamide

(BTSA), a new building block for organic semiconducting materials containing a sulfonamide moiety as key functionality, was designed and developed as a new photovoltaic

materials component by Marks group. This functional group was previously unknown

in OPV materials design. An optimized synthesis of BTSA core was achieved during

the past year, and provides a very convenient 3-step procedure (Figure 9.3A). The high

overall yield, simplicity of the 3-step procedure, and possibilities for upscale production let

one to recognize the methodology toward BTSA as one of the shortest and most efficient,

including cost-efficiency, synthesis of building blocks for semiconducting materials. It

should be noted that the majority of new building block for OPV materials reported now in

literature tend to have more and more sophisticated structures undoubtedly defeating the

purpose of organic semiconductors to be easy available and cost-efficient. Note, functionalization of BTSA with solubilizing chains and groups required for polymerization also

highly efficient and does not require any expensive reagents and catalysts (Figure 9.3B).

Molecular material (SM) comprising of BTSA unit flanked with bithiophene groups was

synthesized (Figure 9.3C). The study of single crystal structure of SM, first proved the

results of DFT calculation used for BTSA design and modeling (see CHiMaD Annual

Report 2014) second, revealed important feature of key sulfonamide unit (SO2N) to form

a dense network of short contact enhancing intermolecular interaction and affection the

packing (Figure 9.3C).

Family of BTSA co-polymers with di- and tri-thiophenes donor units was synthesized and

characterized (Figure 9.4). Representative examples of each group are materials FM0183

(BTSA-2T) and FM0318 (BTSA-3T) D-A polymers were synthesized and characterized.
9.3 Research Accomplishments 85

Figure 9.3: (a) Optimized Synthesis and (b) Functionalization of the Bithiophenesulfonamide

(BTSA) Core; (c) Synthesis of a BTSA-based Small Molecule (SM) and its Single Crystal Structure

OFETs were fabricated for both materials to study the charge transport. For both material

hole mobilities (μh) are in the range of highest reported for donor organic materials used

in BHJ OPV cells. Interestingly, FM0183 films grown on OTS treated Si/SiO2 substrates

demonstrate high crystallinity in film XRD experiments (out-of-plane direction) and have

no features in DSC experiments, which is opposite for FM0318. The material has no

diffraction peaks in films XRD experiments (out-of-plane) but possess sharp melting and

crystallization transitions. OPV cells were fabricated for both polymers using PC71BM as

an acceptor. Both materials exhibit high non-optimized PCE clearly indicating the high

potential of BTSA unit for further development (Figure 9.4).

Structures and Properties of Donor Material Polymers Galli and Goldey (UC) has

examined the structures and properties of the donor polymers poly-3-oxothieno[3,4-

d]isothiazole-1,1-dioxide/ benzodithiophene (PID2) and polythieno[3,4-b]-thiophene/ benzodithiophene (PTB7) and the acceptor material [6,6]-phenyl C71 butyric acid methyl

ester (PC71BM). PTB7 and PID2 are approximated as 1-dimensional periodic systems

using plane wave DFT, with two monomers in the unit cell, approximating 2-ethyl hexyl

side chains as methyl groups. Energy levels using the B3LYP density functional match

experimental cyclic voltammetry values well [Lu, L., et. al.(2014). Nature Photon., 8(9),

716?722], with errors of 0.1 eV, which is visible in Figure 9.5. The groups reported energy

levels for these materials reflect the lowest energy geometries which have calculated. The

band gaps for dihedral conformers of PTB7 and PID2 are presented in Table 9.1. Backbone

twisting for these materials can raise the band gap by 0.5 eV, indicating that local structural

order significantly changes electronic structure.

Interfaces Between Donor and Acceptor Material Polymers Pi-stacked interfaces between

single donor material polymers and the acceptor material have been computed by Galli

group using van der Waals-corrected density functionals, which reflect crystalline regions,

providing pi-stacking distances of 3.79 Å for PTB7 and 3.95 Å for PID2, which are in
86 Chapter 9. Organic Bulk Heterojunction Polymer Solar Cells

Figure 9.4: BTSA-2T polymer (FM0183): (a) Optical Absorption; (b) OFET Transfer characteristics; (c) and D J-V Curve and EQE spectrum. BTSA-3T polymer (FM0318): (e) Optical absorption;

(f) and (g) J-V Curve and EQE spectrum

Figure 9.5: Energy levels (eV) for three organic photovoltaic materials from experiment [Lu, L., et.

al.(2014). Nature Photon., 8(9), 716-722] and theory (periodic plane-wave B3LYP).
9.3 Research Accomplishments 87

PTB7 Conformer α1 α2 β1 β2 Eg@B3LYP (ev)

cis-trans 54 54 133 133 2.29

cis-cis 21 22 26 20 1.81

trans-cis 136 114 41 72 2.32

trans-trans 152 153 158 150 1.82

PID2 Conformer α1 α2 β1 β2 Eg@B3LYP (ev)

cis-trans 47 44 137 139 2.27

cis-cis 9 10 14 14 1.93

trans-cis 138 118 40 70 2.42

trans-trans 154 157 158 150 2.05

Table 9.1: Backbone sulfur-sulfur dihedrals (in degrees) and B3LYP band gaps (eV) for PTB7 and

PID2 donor polymers. The cis-cis conformer was calculated to be the ground state configuration

for both materials.

good agreement with experimental pi-pi spacings from grazing incidence wide-angle x-ray

scattering values (3.79 Å for PTB7) [Szarko, J. M., et. al (2010). Adv. Mater., 22(48),

5468-5472]. Since experimental structures are largely amorphous, characterization of

structural disorder must be performed using statistical sampling of molecular dynamics

trajectories. Our use-case group collaborators, Reid (UC) and de Pablo, have provided us

with several such trajectories (300K, NPT, 200 snapshots, spaced 1000 femtoseconds apart),

with which we have assessed the effect of twisting, bending, and stacking of these polymer

chains on the frontier molecular orbital energies (using the PBE density functional), which

is visible in Figure 9.6. Side-chain and backbone twisting broaden computed energy levels

with standard deviations of 0.2 eV. The inter-chain hole transfer matrix elements, shown

in Figure 9.6 in the inset, have been computed from the frontier molecular orbitals of the

stacked chain configurations.

Molecular Simulation of OPV-relevant Materials Molecular simulation of OPV-relevant

materials presents significant challenges compared to that of many non-crystalline polymer

systems. To assess charge transfer and overall device efficiency, details of both pi-pi stacking between polymers at small scales, and the arrangement of polymers and fullerenes at

large scales is required. While traditional atomistic simulations provide the high resolution

needed to assess nano-scale properties, they cannot easily approach the time and length

scales relevant to overall film morphology and performance. The de Pablo group thus

addressed the problem of simulating OPVs from two fronts: atomistic simulations to

capture molecule-level arrangement, and highly coarse-grained simulations to model larger

scale morphologies. Once a coarse-grained morphology is generated, a detailed atomistic representation can be grown in, allowing investigation of large-scale experimentally

relevant configurations.

To generate atom-level configurations, an accurate force field is essential. However,

standard force fields such as OPLS (Optimized Potentials for Liquid Simulations) do

not describe the highly conjugated molecules used in OPV. We thus combine electronic

structure calculations with relevant OPLS parameters to generate a new force field which

can accurately describe OPV materials. We focus our efforts on simulating PTB7 and

PCBM71, as a starting point for investigating more complex materials. Frequency analysis

of PTB7 shows low frequency modes exist only between rings of the conjugated polymer,

and high frequency modes (relevant only at extremely high temperatures) exist only within
88 Chapter 9. Organic Bulk Heterojunction Polymer Solar Cells

Figure 9.6: Computed PBE energy levels (in eV) for structures from molecular dynamics simulations of PTB7, using one chain (a) and two chains (b). Box plots indicate the middle two quartiles

for the data, and average band positions with standard deviations of the mean are present above the

relevant box plot. Hole transfer inter-chain coupling elements (in meV) for the pi-pi stacked PTB7

chains are presented in the inset graph.

Figure 9.7: Configuration of a single PTB7 molecule in vacuum. When equilibrated, these

molecules show varying degrees of ring stacking based on their length, and steric hindrance due to

sidechains.
9.3 Research Accomplishments 89

Figure 9.8: Bulk PTB7 configuration. Polymers are represented as bead-chains, with stacked beads

colored red. As can be seen, a fair degree of ring stacking exists, with much of it being between

different chains.

the rings. Thus, the rings of the PTB7 are held rigid, while parameters corresponding to

torsion and bending between rings are parameterized using B3LYP/6-311++g method and

basis set. We find out-of-plane bending to be 15 times more stiff than torsion between the

rings, leading to a high persistence length. A similar parameterization process is followed

for PCBM71, where known parameters are taken from OPLS, high frequency modes are

held stiff, and low frequency modes are parameterized using electronic structure techniques.

To verify the accuracy of this force field, we investigate the density, glass transition,

and resulting morphology of materials produced using the newly-constructed PTB7 and

PCBM71 force fields. Experimental values of the glass transition temperature for PTB7

range between 55 and 95◦C. We find a glass transition temperature of 65◦C. Experimental

density of PTB7 is estimated at 1.24 g/cm3. We report two densities. In a perfectly

ring-stacked configuration, our model of PTB7 shows a density of 1.28 g/cm3, while a

bulk simulation of PTB7 cooled from a melt over 5 nanoseconds shows a density of 1.11

g/cm3. Experimental films are formed over the course of minutes, so lower densities in

simulated films are expected.

While charge can transport fast along a polymer chain, inter-chain charge transport is

necessary in order to reach the appropriate electrode. De Pablo investigated the ring

stacking behavior of single PTB7 molecules with and without its sidechains, and that

of PTB7 in the bulk. Single PTB7 molecules without sidechains show highly coiled

configurations with on average 70% of the rings stacked for 50-mers (Shown in Figure 9.8),

while steric hindrance in the full PTB7 molecule (with sidechains) reduces this value to

30%. Moving to stacking in the a bulk material of PTB7, we find that improved solvation,

as compared to PTB7 in the vacuum, increases end-to-end distance by a factor of 3 (for

50-mers) and increases stacking to 70%. For efficient charge transfer, polymers must

stack with other polymers, not only themselves. We find that in the bulk material, 40% of

stacking is with other chains, while 60% of stacking interactions are with itself (Figure

??).
90 Chapter 9. Organic Bulk Heterojunction Polymer Solar Cells

9.4 Collaborations

9.4.1 CHiMaD Collaborations

MD force field generation for Organic Bulk Heterojunction Polymer Solar Cells

Guilia Galli (UC), Juan de Pablo (UC)

The collaboration between Galli and de Pablo groups has focused on the generation and

validation of an atomistic force field for simulations of the donor polymer PTB7. Several

potential energy surfaces for calibration of internal coordinate parameters were calculated.

Galli group has also used trajectories with this force field to statistically sample geometries

for their electronic structure calculations.

Polymer Solar Cells

Monica Olvera de la Cruz (NU), Tobin Marks (NU)

Through this collaboration, Olvera and Marks groups are developing new design principles for electron accepting polymers and to establish the structure/property relationship of

these n-types of organic polymers for the purposes of solar energy conversion and organic

electronic applications. The goal of this work is to generate new materials that will exhibit

a greater potential than existing fullerene derivatives as n-type semiconducting materials.

Data Mining for Design and Development of OPV Materials

Luping Yu (UC), Guilia Galli (UC), Ankit Agrawal (NU), Alok Choudhary (NU), Christopher Wolverton (NU)

During the CHiMaD Materials Design Workshop I use-case presentation by Goldey (UC)

and Schneider (UC), a possible collaboration between the Organic Bulk Heterojunction

Polymer Solar Cells and Data Mining use-case groups came to light. After initial discussions between use-case groups including Goldey (UC), Schneider (UC), Ward (NU),

Furmanchuk (NU), Paul (NU), it was decided that there is little correlation between device

efficiency and energy levels of polymeric material, but stronger correlations between power

conversion efficiency and morphology-dominated properties.The groups will be exploring

the possibility of using data mining for determining the uncontrolled morphology of active

layer in order to support building of OPV devices.

9.4.2 NIST Collaborations

Energy Level Validation for Organic Bulk Heterojunction Polymer Solar Cells

Guilia Galli (UC), Luping Yu (UC), Lee Richter (NIST)

This collaboration involved the measurement of ultraviolet photoelectron spectroscopy

values for donor and acceptor material ionization potentials and electron affinities. Yu

group has provided the materials, and Richter (NIST) and his experimental colleagues at

NIST are measuring the glass transition temperature, UPS for determining HOMO/LUMO

energy levels and the origin of Voc/composition correlation in ternary solar cells levels

to validate the theoretical best estimates of material energy levels generate by the Galli

group.

New Atomistic and Coarse-Grained Models

Juan de Pablo (UC), Luping Yu (UC), Tobin Marks (NU), Giulia Galli (UC), Lee Richter

(NIST)

In collaboration with Yu, Marks, Galli and Richter (NIST), the de Pablo group has developed new atomistic models and coarse-grained models for description of OPV materials.

The results of these efforts were presented at a workshop organized in November at the
9.5 Publications and Presentations 91

University of Chicago that involved several leading OPV researchers from around the

world, including three participants from NIST.

9.4.3 External Collaborations

GWIXS of Ternary OPV Systems

Luping Yu (UC), Wei Chen (Argonne National Laboratory)

This collaboration with Chen group at ANL involves the experimental efforts regrading

Grazing incidence wide-angle X-ray scattering (GWIXS) measurements of OPV systems

this use-case is working on.

Ultrafast Spectroscopy

Luping Yu (UC), Lin Chen (Argonne National Laboratory/ Northwestern University)

This collaboration with Chen group at ANL involves the experimental efforts regrading

ultrafast spectroscopy measurements of OPV systems this use-case is working on.

9.5 Publications and Presentations

8

Number of CHiMaD-supported publications in 2015 by Organic Bulk Heterojunction

Polymer Solar Cells use-case group. Please see chapter 20 for details. [3, 6, 7, 8, 34,

35, 36, 37]

13 Number of presentations on CHiMaD supported research in 2015, please seesection 21.8 for the complete list.

1

Organic Bulk Heterojunction Polymer Solar Cells use-case group organized an international symposium entitled Advances and Challenges in Soft Matter Photovoltaic

Research held at Institute of Molecular Engineering, University of Chicago on November

13-14, 2015.

9.6 CHiMaD Team

Organic Bulk Heterojunction Polymer Solar Cells

Use-Case Group Researchers

Name Position Affiliation Project (PI)

Luping Yu Principal Investigator UC/CHEM

Juan de Pablo Principal Investigator UC/IME

Guilia Galli Principal Investigator UC/IME

Tobin Marks Principal Investigator NU/MSE

Matthew Goldey Postdoctoral Researcher UC/IME Organic bulk heterojunction polymer solar cells (Galli)

Daniel Reid Graduate Student UC/IME Simulation of OPV materials (de

Pablo)

Alexander Schneider Graduate Student UC/CHEM Organic bulk heterojunction polymer solar cells (Yu)

Luyao Lu Graduate Student UC/CHEM Novel materials for OPV applications (Yu)

III

10 Additive Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

11 Coarse Grained Simulations of Organic Polymer Solar Cells . . . . . . . . . . . 99

12 Impact Mitigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

CHiMaD Seed Groups

10. Additive Manufacturing

Jian Cao (NU)

10.1 Significance & Research

Additive manufacturing breaks three fundamental manufacturing tradeoffs: capital versus

scale, capital versus scope, and capital verses complexity. It also significantly reduces

material waste by placing materials only where needed. Widespread acceptance of additive

manufacturing will increase supply chain efficiency, reduce time to market, move from

mass production to mass customization, and sustain the environment. This will spur a

technology renaissance by bridging manufacturing of all scales with material design and

will broaden participation by disseminating accessibility to cutting-edge manufacturing

processes.

Figure 10.1: Laser Deposition

Powder flow is a critical element in controlling the

quality of parts made by powder deposition process,

one additive manufacturing process. Rapid on/off

powder control has been achieved, which has virtually eliminated the material transport delay by reducing it from 24.8 seconds to 0.20 seconds. This

control system allows powder start and shutoff strategically under the control to reduce the waste of both

powder and machine time. Additionally, it reduces

the inadvertent adhesion of loose powder to the component. More interestingly, the precise powder control also allows for rapid toggling between materials

in multi-material builds.

The next step in powder control, with the ultimate

goal of creating an integrated multi-parameter control of laser powder deposition processes to explore the material design freedom and

capability, is to enable analog metering control so that the powder deposited per unit

length may be maintained uniformly. The metering solution will require the design and

manufacture of a custom valve. Once the metering hardware system is online, the system
96 Chapter 10. Additive Manufacturing

variables can be experimentally determined and the controller can be tuned. The powder

control system will then be implemented and its performance will be evaluated.

10.2 Collaborations

10.2.1 CHiMaD Collaborations

Additive Manufacturing of Co-Alloys

Jian Cao (NU), Gregory Olson (NU), Wing-Kam Liu (NU)

The graduate students from the three groups are working in collaboration on the topic of

additive manufacturing.

10.2.2 NIST Collaborations

Additive Manufacturing

Jian Cao (NU), Wing-Kam Liu (NU), Gregory Olson (NU), Lyle Levine (NIST), Li Ma

(NIST), Carelyn Campbell (NIST), Mark Stoudt (NIST)

There is an active communication and exchange of research findings between these groups.

10.2.3 External Collaborations

Collaboration with DMG-MORI USA

Jian Cao (NU)

An industrial collaboration on machine control has been established with DMG-MORI,

USA, worth over $1B.

10.3 Publications and Presentations

4

Number of CHiMaD-supported publications in 2015 by Additive Manufacturing seed

group. Please see chapter 20 for details. [21, 22, 23, 24]

2

Number of presentations on CHiMaD supported research in 2015, please see section

21.10 for the complete list.

2

Additive Manufacturing seed group has worked with Campbell (NIST) to co-fund

the National Academy of Sciences workshop entitled Predictive Theoretical and

Computational Approaches for Additive Manufacturin held at Washington D.C. on October

7-9, 2015; presentation slides and additonal videos are available at

htt p : //sites.nationalacademies.org/PGA/biso/IUTAM/PGA168737.

Cao also organized the Roadmapping for Innovation in Sheet Metal Forming Workshop on

May 4, 2015.
10.4 CHiMaD Team 97

10.4 CHiMaD Team

Additive Manufacturing

Seed Group Researchers

Name Position Affiliation Project (PI)

Jian Cao Principal Investigator NU/ME

Jennifer Bennett Graduate Student NU/ME Flow Control in Additive Manufacturing to Enable Building

Functionally Graded Materials

(Cao, Ehmann (NU))

11. Coarse Grained Simulations of Organic Polymer Solar Cells

Monica Olvera de la Cruz (NU)

11.1 Significance & Research

Investigating polymer-polymer blend film morphology via coarse-grained simulations

provides a different and complementary perspective to experrimentation, for better understanding of the physical processes that occur during blend film formation.

De la Cruz group performed coarse grained simulations of wormlike chains to elucidate

the influence of number-average molecular weight (Mn) on the blend film morphology

and photovoltaic performance of all-polymer solar cells fabricated with the donor polymer

PTPD3T and acceptor polymer N2200 (see Publication [38]). The simulations showed that

the Mn variation of both PTPD3T and N2200 polymers greatly influences both intrachain

and interchain correlations, the degree of phase separation and morphology of the blend.

The experimental study showed that increasing Mn for both polymers shrinks blend film

domain sizes and enhances donor-acceptor polymer-polymer interfacial areas, which leads

to an increase in short-circuit current densities (Jsc). However, when Mn increases the

degree of chain entanglement increases, which promotes charge trapping in intramolecular

charge transport, reducing cell fill factors (FF). Therefore, there is an optimized Mn for

well-balanced exciton dissociation and charge transport characteristics, which involves

increasing both polymers Mn such that they both have very close values of Mn.

The TEM morphologies are in agreement with the simulations results, summarized in

Figure 1. The lowest Mn of both polymers is strongly phase-separated with large fibril-like

polymer microstructures. Such morphologies are known to introduce large grain boundaries

with poorly connected polymer networks, suppressing charge carrier transport. For medium

Mn of both polymers the film morphologies begin to show more intermixed features, which

would enable more efficient exciton scission. This is accompanied by relatively continuous

polymer domains, which can be critical for efficient charge transport. Finally, for the

highest Mn polymers, the blend films display highly disordered characteristics, with the

least degree of semi-crystalline polymer domains.
100 Chapter 11. Coarse Grained Simulations of Organic Polymer Solar Cells

Figure 11.1: MD simulations of all-polymer blend morphology. a)

Polymer-polymer blend film morphology from coarse-grained MD

simulations. The n-type polymer is represented by red beads and ptype polymer is by grey beads. b) Left (right): Number of monomers

of p- (n-) type at a certain distance from monomer of n- (p-) type

averaged among all monomers of p- (n-) type. The distance between

a monomer of p- (n-) type to its nearest neighboring monomer of

type n- (p-) is measured.

Investigating polymer-polymer

blend film morphology

via simulations provides

a different and complementary perspective, for

better understanding of

the physical processes that

occur during blend film

formation. Considering

that the phase separation feature sizes are a

few tens of nm, which

is prohibitively large in

full atomistic simulations,

large-scale molecular dynamics (MD) simulations

were employed on a general coarse-grained model

of blends of the two polymers. This model provides an excellent platform to analyze the morphological characteristics

of the polymer-polymer

blends. In this model, each

monomer unit is coarsegrained into a single bead

of size σ, connected via a

bond potential, Ubond(r) =

1

2

kbond(r −r0)

2

, and an angle potential, Uangle(θ) =

1

2

kangle(θ −π)

2

, to a nearby

monomer unit. The bond

rest length r0 is arbitrarily set at 0.84σ to avoid bond crossings. Interactions for nonbonded beads are modeled by a shifted Lennard-Jones potential, which is written as

ULJ(ri j) = 4ε[( σ

ri j

)

12 −

σ

(

ri j)

6

)] −ULJ(rcut) for r ≤ rcut (rcut = 3.0σ) and ULJ(ri j) for

r > rcut. σ ≈ 1.6 nm specifies the unit of length, and the Lennard-Jones potential wells for

the description of monomer interactions are εAA = 0.5ε0; εAB = 0.3ε0; εBB = 0.1ε0, where

ε0 ≈ 0.38 kcal mol−1represents the unit of energy in our simulations. The condition of

εBB < εAB is used as εBB > εAB will drive the polymer blend to phase separate instead

of phase separation. Constraints in kbond = 330ε0 and kangle = 20ε0 describe reasonable

rigidity for such worm-like chains. An extra layer of fixed beads acting as a boundary

condition for the bottom of the simulation box is included to model the film substrate. The

model presented here can provide general insights into OPV polymer selection beyond the

specific n- and p-type polymers employed in this work.
11.2 Collaborations 101

11.2 Collaborations

11.2.1 CHiMaD Collaborations

Polymer Solar Cells

Monica Olvera de la Cruz (NU), Tobin Marks (NU)

Through this collaboration, Olvera and Marks groups are developing new design principles for electron accepting polymers and to establish the structure/property relationship of

these n-types of organic polymers for the purposes of solar energy conversion and organic

electronic applications. The goal of this work is to generate new materials that will exhibit

a greater potential than existing fullerene derivatives as n-type semiconducting materials.

11.3 Publications

1

Number of CHiMaD-supported publications in 2015 by Coarse-grained simulations

of Organic Polymer Solar Cells seed group. Please see chapter 20 for details. [38]

5

Number of presentations on CHiMaD supported research in 2015, please see section

21.11 for the complete list.

11.4 CHiMaD Team

Coarse Grained Simulations of Organic Polymer Solar Cells

Seed Group Researchers

Name Position Affiliation Project (PI)

Olvera de la Cruz Principal Investigator NU/MSE

Victor Pryamitsyn Postdoctoral Researcher NU/MSE (Olvera)

12. Impact Mitigation

Heinrich Jaeger (UC), Sid Nagel (UC), Juan de Pablo (UC)

12.1 Significance & Research

The damaging effects of blunt impact on human physiology and health have been most

notable in the manifestation of the high rates of chronic traumatic encephalopathy (CTE)

in athletes, particularly football players. While this is the focus of current sports injury

prevention, impacts to soft tissues are the basis for many chronic joint and musculoskeletal

diseases. In team sports athletes rely on pads of geometrically structured, non-linear,

time-dependent polymeric materials (e.g. elastomers, foams, thermoplastics, gas bladders,

etc.), to absorb energy, limit momentum transfer, and reduce force at velocities that can

exceed 45 m/s and energies up to 75 J. In many cases such materials are clearly inadequate,

and there is a pressing need to reduce impact injuries to the head and body from a wide

variety of threats in sporting, industrial, and military applications. Advances in protective

materials have been incremental due to a lack of dynamic material property data and a lack

of predictive models to spur innovation. The design goals of this Seed group are to develop

innovative materials for impact mitigation that will be superior to those available today,

and to do so by relying on predictive models coupled to high-precision experimentation in

the context of both liquid and solid shock absorbing systems.

Several critical needs that currently limit progress are being addressed in this work:

Novel Metrologies for Multiaxial Impact with Distributed Force Measurements Coupled

to Measures of the Time-evolving Volumetric Strain Fields Inside And On The Surface

of a Material As It is Loaded At High Rates. Jaeger group has developed the capability

to measure such fields in colloidal suspensions that can absorb impact at ultrafast rates.

Figure 12.1 shows a representative measurement of the strain, measured using ultrasound

techniques at frame rates of 10,000 per second, as it propagates through a starch suspension

immediately after a blunt impact. Moving forward, de Pablo and Jaeger groups will

develop multiscale modeling techniques based on concepts from Stokesian dynamics,

friction dynamics, and jamming, that will be used to interpret experimental data, to identify

key design principles, and to optimize materials for specific applications.
104 Chapter 12. Impact Mitigation

Figure 12.1: Ultrasonic measurement of strain inside dense, opaque suspensions of starch, shortly

after blunt impact. Images taken at 10,000 frames per second (Jaeger group).

Figure 12.2: Ultrasonic measurement of strain inside dense, opaque suspensions of starch, shortly

after blunt impact. Images taken at 10,000 frames

per second (Jaeger group).

Development of Innovative Materials

Strategies By identifying regions of a material that exhibit high or low local shear

and bulk modulus, the Nagel group can selectively eliminate (or prune) such regions

in order to arrive at systems that exhibit

auxetic behavior and that are capable of

dissipating strain in controlled directions.

Such materials are currently being designed

by de Pablo and Nagel groups by relying

on computer models that identify weak or

strong regions in the context of jammed

particulate samples. Figure 12.2 shows an

example of a macroscopic two dimensional

material that was designed on the computer

and subsequently prepared by laser cutting.

The material exhibits a negative Poisson ratio, and the predicted deformation agrees

quantitatively with that observed in experiments. Moving forward, a new computational platform, based on concepts from artificial evolution, will enable material discovery

of novel architectures and faster material optimization, in two and three dimensions.

The successful execution of this proposal will provide scientists with the capabilities to

exploit truly innovative concepts in energy dissipation such as force deflection and interface

slip to further increase performance and tailor materials and architectures for the impact

frequencies and energies that represent the highest risk for injury.
12.2 Collaborations 105

12.2 Collaborations

12.2.1 NIST Collaborations

Development of Novel Metrologies

Juan de Pablo (UC), Sid Nagel (UC), Heinrich Jaeger (UC), Chealsea Davis (NIST),

Aaron Foster (NIST)

The efforts of this seed group complement those at NIST by Foster (NIST) and Davis

(NIST), who are developing metrologies for multiaxial dynamic loading coupled with

distributed force measurement, and volumetric exterior strain measurements. Both institutes have expertise in materials for energy dissipation. A collaborative visit by Foster

(NIST) and Davis (NIST) to Chicago in February of 2016 was used to identify specific

targets for the immediate future collaborations that will involve joint experiments, as well

as exchange of students and senior personnel between CHiMaD and NIST.

12.3 CHiMaD Team

Impact Mitigation

Seed Group Researchers

Name Position Affiliation

Juan de Pablo Principal Investigator UC/IME

Sid Nagel Principal Investigator UC/PHY

Heinrich Jaeger Principal Investigator UC/PHY

IV

13 Materials Data Facility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

14 Phase Field Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

CHiMaD Tools

13. Materials Data Facility

Ian Foster (UC)

Significance

Materials innovation has been a critical component and driver of economic prosperity

and competitiveness for millennia. Yet, the process by which new materials are designed,

developed, manufactured, and deployed remains incredibly slow and inefficient. With the

Materials Data Facility (MDF), we aim to develop and deploy a set of flexible services that

will support modern materials research by allowing researchers to publish and discover

datasets regardless of size and from a variety of data storage endpoints. The MDF services

will allow users to publish, share, discover, access, and obtain data from past experiments

and simulations and facilitate data exchange mechanisms to help prevent redundant work,

to encourage broader data synthesis where appropriate, and to begin building the data

infrastructure necessary to realize in silico materials discovery. In a cross-disciplinary

center, such as CHiMaD, data sharing and data availability (e.g. between computational

researchers and experimental researchers) will play a key role in driving effective and

impactful collaboration. Beyond the immediate benefits to CHiMaD, MDF will play a

role in ensuring that the scientific results from CHiMaD are made widely available to the

community for years to come. We also see opportunities to partner with researchers at

NIST to improve the discoverability and availability of a variety of reference datasets of

particular interest to the materials research community.

13.1 Design Goals

The Materials Data Facility will investigate next generation materials database and data

sharing techniques to support the MGI and NIST goals, and deploy and operate cloudhosted services and other data resources to support CHiMaD collaborations and the

materials science research community more broadly. The initial MDF Year 1 focus is on

data publication, with an emphasis on building the necessary web interfaces and APIs to

support researcher self-service data publication from a variety of data storage endpoints

(e.g. institutional storage, laboratory data servers, Amazon S3 cloud resources, etc.) to
110 Chapter 13. Materials Data Facility

Figure 13.1: Dataset submission workflow in MDF. (clockwise from the upper left) Creating a new

submission, choosing a collection to add the submission to, describing the item with customizable

metadata fields, and assembling the underlying dataset

simplify data sharing, archiving, and description of datasets to increase transparency and

encourage data reuse. Additionally, we are operating a storage allocation at NCSA to begin

ingesting datasets in early 2016, and we are developing the required infrastructure and

tools to allow indexing and deep discovery of distributed scientific datasets via centralized

search.

13.2 Materials Data Facility

The Materials Data Facility is a set of services designed to simplify the process by which

materials science researchers publish, discover, share, and reuse datasets. The primary

focus of the first phase of the MDF project is the deployment, operation, and iterative

improvement of a data publication service, provided as a centralized service to reduce cost

to operate, and to reduce friction to drive adoption by institutions and researchers. With

MDF, users will be able to publish and discover data via a web user interface (UI) or programmatically via associated REST interfaces (in development). User identity, user group

management, and data transfer have been integrated with existing production-level services

already provided by Globus [Foster, IEEE Internet Computing 3 (2011): 70-73]. Collections within MDF contain logically similar datasets, and allow for configurable workflows,

metadata forms, storage endpoints, dataset unique identifiers, and other collection-specific

policies. Datasets within collections are described by the prescribed collection-specific

metadata schema, and have an associated data endpoint where raw and derived data are

stored. Importantly, while initial storage for the MDF pilot has been allocated at NCSA

(UIUC), the data endpoints can be at any location capable of hosting a Globus endpoint:

i.e. locally-hosted storage, repository-specific storage, institutional storage, or even cloud

storage on Amazon S3. After a dataset is assembled, the associated metadata is indexed to

facilitate materials dataset discovery.
13.3 Future Outlook 111

13.2.1 Publishing and Discovering Data with MDF

As of December 2015, we have deployed a production data publication service and allocated 100TB of reliable networked storage at National Center for Supercomputing

Applications (NCSA) to support our initial users. We are now in the process of identifying

key datasets, particularly those associated with CHiMaD researchers, to demonstrate the

end-to-end data publication flow. Efforts are ongoing to develop metadata schemas and

controlled vocabularies, in conjunction with researchers at NIST, to describe materials

resources at a general level (e.g. experimental technique, material type, material morphology, etc.). We are also working to improve MDF metadata schema creation interfaces, one

of the remaining challenges before the publication process can be considered self service.

Data published with the MDF data publication service is able to be identified (e.g. associated with a persistent DOI or Handle); described with custom metadata; and curated

via custom workflows. These datasets are made accessible via Globus (to handle large

datasets) and via HTTP (in development for smaller datasets), searchable via free text, and

browsable by faceting, tag, community, and collection exploration.

Data publication in MDF follows a workflow as shown in Figure 13.1. From the users data

publication dashboard, which also displays information on the users prior and pending

publications, the user chooses to initiate a new publication. The user then chooses the

appropriate collection to publish the dataset into. Administrators can configure and

customize the requirements for a collection with settings such as customized metadata

fields, specified curation workflow (e.g. each submission must be reviewed by the collection

administrator), and a specified data storage location. After selecting the appropriate

collection for his publication, the user enters the predefined, collection-specific metadata

(e.g. Datacite or domain-specific fields). Following this step, the dataset is assembled using

Globus transfer to move selected files to a folder that is created on the collection-specified

storage. The user assembles the dataset asynchronously using Globus transfer, and may

return to complete the dataset submission when the transfer of the data is complete. Upon

completion of dataset assembly, the user is presented with a summary page to confirm

publication details (i.e. both data contents and metadata records) and once accepted, the

dataset is optionally routed through the specified curation workflow.

After the dataset curation workflow is complete, the dataset is indexed and discoverable

with external dataset access granted as determined by the collections pre-determined access

control policy. Free text search and automatically generated facets on the MDF portal page

help users find the datasets they are searching for. An example of a free text MDF search

is shown in Figure 13.2. Matching datasets are returned as results and facets on author and

subject are generated to help users navigate through the result set.

13.3 Future Outlook

A primary goal of MDF is to provide simple, reliable, and robust services tailored to

the specific needs of the materials science community. Towards that end, it is critical

to build relationships and interact with materials scientists in a variety of disciplines to

understand their publication, discovery, and data sharing requirements, and to understand

search requirements and other features of the associated use cases. We plan to enhance

search and discovery capabilities by allowing users to specify which metadata attributes are

indexed, which are shown as facets, and which are displayed in search results and summary

pages. As a longer-term goal, we will seek to expand the flexibility of the metadata model
112 Chapter 13. Materials Data Facility

Figure 13.2: Searching across MDF collections by free text. Search results, comprised of published

datasets, are shown on the left with additional facets on subject and author to allow quick navigation

through the results

to support metadata objects, to allow typed metadata, and to support per-file specified

metadata. We are also working to make many of the MDF features accessible via REST

API to support programmatic access to published data, associated curation workflows,

and to the exposed discovery features. These APIs will support a variety of new use

functionality including automated ingest of metadata from file types that are critical to

materials science to minimize the amount of metadata that scientists need to add manually.

13.4 NIST Collaborations

General Metadata Schema for Describing Materials Data Resources

Ian Foster (UC/ANL), Robert Hanisch (NIST), Chandler Becker (NIST), Raymond Plante

(NIST)

In collaboration with Hanisch (NIST), Becker (NIST), and Plante (NIST), we are working

to iteratively draft, adopt, and provide feedback on a general metadata schema for describing materials data resources. A general metadata schema would provide a standardized set

of fields to ingest and index, and provide a starting point for building specialized custom

metadata schemas.

Synergy with NIST Efforts

Ian Foster (UC/ANL), Mary Brady (NIST), Alden Dima (NIST), Carelyn Campbell (NIST),

Sharief Youssef (NIST)

In collaboration with Brady (NIST), Dima (NIST), Campbell (NIST), and Youssef (NIST),

we are in the early stages of investigating the potential to build a deeper connection between

the MDF data publication service and the NIST Materials Data Curation System (MDCS)

and we are working to deploy a Materials Resource Registry (MRR) instance to catalog

and make discoverable relevant software, databases, and APIs.
13.5 Technology Transfer 113

13.5 Technology Transfer

Data Services

Materials Data Facility

I. Foster, S. Tuecke, B. Blaiszik, K. Chard, J. Pruyne, M. Ondrajcek

Access Link: https://materialsdatafacility.org

13.6 Publications and Presentations

2

Number of CHiMaD-supported publications in 2015 by Materials Data Facility

Developers. Please see chapter 20 for details. [39, 40]

5

Number of presentations on CHiMaD supported research in 2015, please see section

21.9 for the complete list.

13.7 CHiMaD Team

Materials Data Facility

Developers

Name Position Affiliation

Ian Foster Principal Investigator UC/CI

Steven Tuecke Deputy Director UC/CI - ANL/MCS

Benjamin Blaiszik Software Developer UC/CI - ANL/MCS

Kyle Chard Senior Researcher/Fellow UC/CI - ANL/MCS

Jim Pruyne Senior Software Developer UC/CI

Michal Ondrejcek Research Programmer UIUC/NCSA

14. Phase Field Methods

Olle Heionen (NU), Peter Voorhees (NU), David Chopp (NU), Barry Smith (ANL),

Dmitry Karpeev (ANL)

Significance

Mesoscale materials modeling, that spans length and time scales larger than atomic but

smaller than microscopic, is central to modeling materials structural evolution. This is

particularly the case for systems in which several degrees of freedom, such as elastic strain

and diffusion, are coupled in inhomogeneous materials structures. Phase field methods

are one set of mesoscale approaches to deal with such systems. CHiMaD is advancing

phase fields methods with a goal of having quantitatively predictive modeling capabilities

for structural evolution of three-dimensional systems. The efforts span both development

of computational methods, algorithms, and models, as well as developing community

standard problems for benchmarking, verification, and validation of phase field codes.

14.1 Research Tools

3D Multiphysics Phase Field Model Heinonen developed a 3D multiphysics phase-field

model to study phase segregation in LiFePO4 nanoparticles, of interest due to their high

(dis)charge rates in battery electrode applications. Spinodal decomposition into Li-rich and

-poor phases is modified and can be suppressed by mesoscopic effects, which influences

the kinetic and mechanical performance of this material as a battery electrode. Elastic and

structural constants, diffusivity, and surface energy are highly anisotropic and concentration

dependent, necessitating a 3D treatment.

The model includes spinodal decomposition, anisotropic, concentration-dependent elastic

moduli, misfit strain, and facet dependent surface wetting within a Cahn-Hilliard framework. We simulated 3D spherical particles of radii from 3 nm to 40 nm and examined

the equilibrium microstructure and voltage profiles as a they depend on size and overall

lithiation. Our results show that the miscibility gap vanishes for particles of radius approximately 5 nm, and the solubility limits change with overall particle lithiation. Surface

wetting stabilizes minority phases by aligning them with energetically beneficial facets.
116 Chapter 14. Phase Field Methods

Figure 14.1: Summary of simulation results including surface wetting showing microstructural

evolution as a function of particle radius and average concentration. Only results with the lowest

free energy are shown. Surface wetting causes (de)lithiation of certain areas of the surface and

competes with interfacial area minimization and anisotropic elasticity for dominance in determining

the form and symmetry of the microstructure. Symmetries change for higher concentration x and

are absent for the 40 nm Li0.8FePO4 particle.

Our results point to avenues to further improve the performance of LixFePO4 nanoparticle

electrodes by engineering surfaces with coating or geometry to affect the surface wetting.

Heinonen also developed a model for core-shell nanoparticles to study how the band gap

is distributed in the nanoparticle, and how it can be affected by particle size and grain

orientation, as well as by external pressure. In particular the band gap of Zn-ZnO core-shell

nanoparticles are amenable to band gap modifications.

Community Standard Phase Field Codes The Phase Field Methods seed group researchers

Heinonen, Voorhees are working on developing Phase Field Benchmark problems for

community code development and benchmarking, in collaboration with Warren (NIST),

Guyer (NIST), Smith, Karpeev. They have brought together an elite team of Phase Field

experts and framework developers at the CHiMaD Phase Field Methods Workshops. Please

see 19.5 for more information.

2D Locally Adaptive Time Stepping Method for Phase Field Simulations Chopp group

developed the first 2D implementation of the Locally Adaptive Time Stepping Method

(LATS), and applied it to solving the Cahn-Hilliard equation with periodic boundary

conditions. Currently, the use of the LATS method for this equation is being explored

as a means of enabling longer simulations for the phase field model by concentrating

computational effort just on the subdomains where interfaces are present. Subdomains that

are primarily only one phase will be updated orders of magnitude less often compared to

subdomains where the interface is still present. Thus, as the phases coalesce, the method

will get increasingly more efficient.

The LATS method was originally developed for one-dimensional domains, so the challenge

is to ensure that the more complex boundary conditions between subdomains in a higher

dimensional setting would still work. For testing purposes, a stripe domain decomposition

was used in order to make communications simpler. Chopp group demonstrated both that

(1) the method preserves the stability properties of the underlying backward difference

formula time stepping method, (2) the version with domain decomposition gave the same

results as the corresponding serial implementation, and (3) the method scales appropriately
14.2 Collaborations 117

on a parallel architecture. A version with rectangular domain decomposition is expected to

be complete by the end of CHiMaD Year Two, after which benchmark testing for optimal

time steps will begin.

14.2 Collaborations

14.2.1 NIST Collaborations

Phase Field Benchmark Problems

Olle Heinonen (ANL), Peter Voorhees (NU), James Warren (NIST), Jonathan Guyer

(NIST)

This collaboration aims to develop phase field benchmark problems for the global phase

field community.

14.2.2 External Collaborations

Scalable Computational Models and Codes

Olle Heinonen (ANL), Serge Nakhmanson (University of Connecticut)

Through this collaboration scalable computational models and codes for coupled ferroelectricelastic systems are being developed.

Scalable Computational Models and Codes

Olle Heinonen (ANL), Michael Welland (Chalk River National Laboratory, Canada)

This collaboration concentrated on developing phase field models for coupled diffusionelasticity problems.

14.3 Publications and Presentations

2

Number of CHiMaD-supported publications in 2015 by Additive Manufacturing seed

group. Please see chapter 20 for details. [41, 42]

3

Number of presentations on CHiMaD supported research in 2015, please see section

21.12 for the complete list.

2

Phase Field Methods seed group has organized the CHiMaD Phase Field Methods

Workshop I-II and the CHiMaD Phase Field Hackathon. Please see 19.5 for more

information.
118 Chapter 14. Phase Field Methods

14.4 CHiMaD Team

Phase Field Methods Group Researchers

Name Position Affiliation Project (PI)

Olle Heinonen Principal Investigator ANL /NAISE

Peter Voorhees Principal Investigator NU/MSE

David Chopp Principal Investigator NU/ESAM

Dmitry Karpeev Principal Investigator ANL /NAISE

Barry Smith Principal Investigator ANL /NAISE

Andrea Jokisaari Postdoctoral ResearcherNU/NAISE Mesoscale modeling of

microstructural evolution

(Heinonen)

Narut Sereewattanawoot Graduate Student NU/ESAM LATS method for phase

field simulations (Chopp)
V

15 Honors & Awards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

16 NIST-CHiMaD Postdoctoral Research Fellows . . . . . . . . . . . . . . . . . . . . . . . . 123

17 CHiMaD Principal Investigators and Staff . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

18 CHiMaD NIST Collaborators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

19 CHiMaD Outreach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

Other

15. Honors & Awards

Hadallia Bergeron (NU)

Hierarchical Materials Cluster Program Graduate Fellowship

The Graduate School

Northwestern University

Wei Chen (NU)

Design Automation Award (the highest achievement award in design automation)

Design Engineering Division

American Society of Mechanical Engineers (ASME)

Mark Hersam (NU)

World Technology Network Fellow

The World Technology Network

Sinan Keten (NU)

ONR Young Investigator Program Award

Office of Naval Research

David Seidman (NU)

ASM International Edward DeMille Campbell Memorial Lectureship

Material Science and Technology meeting

Columbus, OH, USA(10/07/15)

Steven Sibener (UC)

Dedicated Festschrift Issue in his honor

36 manuscripts from groups located worldwide

Journal of Physical Chemistry C

J. Phys. Chem. C., July 2 Issue, Vol. 119, No. 26 (2015)

Juan de Pablo (UC)

Elected into National Academy of Engineering

For design of macromolecular products and processes via scientific computation

Juan de Pablo (UC)

Elected into Mexican Academy of Sciences

Mexican Academy of Sciences
122 Chapter 15. Honors & Awards

Juan de Pablo (UC)

Delivered Alumni Lectures

University of Massachusetts

Juan de Pablo (UC)

Delivered Peck Lectures

Illinois Institute of Technology

Juan de Pablo (UC)

Keynote Address

8th PPEPPD (Property and Phase Equilibria for Predictive Process Design) International Conference

Porto, Portugal

Juan de Pablo (UC)

Founding Editor

Molecular Systems Design and Engineering

Alexander Umatsev (FSU)

2015-2016 Fulbright U.S. Scholar Award

J. William Fulbright Foreign Scholarship Board

Peter Voorhees (NU)

Van Vlack Lectures

Department of Materials Science and Engineering

University of Michigan

Peter Voorhees (NU)

Hillert-Cahn Lectures

International Conference on solid-Solid Phase Transformations

Whistler, Canada

Logan Ward (NU)

Weertman Fellowship

Department of Materials Science and Engineering

Northwestern University

Zonghui Wei (NU)

Ryan Fellowship

The Graduate School

Northwestern University

Huanxin Wu (NU)

Fellowship

Center for Computation and Theory of Soft Matter

Northwestern University
16. NIST-CHiMaD Postdoctoral Research Fellows

Marat Andreev (UC/NIST)

Soft Matter Design Based on Charge Complexation

Supervisors: Juan De Pablo (UC), Jack Douglas (NIST)

Bio Dr. Andreev was born in Siberia, Russia. He received his B.Sc

and M.Sc degrees from Novosibirsk State University. In 2009 he

joined Professor Jay Schieber’s research group at Illinois Institute

of Technology and was awarded his Ph.D. in December 2014.

He then joined the De Pablo group at University of Chicago

as postdoctoral researcher; in 2015 he was awarded a NISTCHiMaD postdoctoral research fellowship.

Research Polyelectrolyte coacervation refers to the formation of

distinct liquid phases that arise when polycations and polyanions

are mixed under appropriate polymer and salt concentrations. Molecular-level studies

of complex polyelectrolyte coacervation have been limited. Under supervision of de

Pablo, a computationally efficient soft-core model of the polymers and the corresponding

counterions is proposed. The model is used to simulate coacervation as a function of

polymer length and overall salt concentration. The model is shown to reproduce a number

of phenomena observed in experiments, including the destabilization of the coacervate by

increasing ionic strength of by decreasing molecular weight. The results of the soft-core

model are also shown to compare favorably with those of a more traditional representation

that relies on hardcore potentials. Self-assembly of coacervate in the model is studied

under supervision of Douglas (NIST) during my stay at NIST. A manuscript based on this

work is in preparation.

The soft-core models, such in Figure 16.1, have limitations for application to dense

polymeric systems. Particularly, it is commonly accepted that polymer chains over a certain

molecular weight form so-called entanglements-topological constraints that chains exert on

each other. These entanglements are further believed to govern the macroscopic behavior

of polymeric systems. It is possible to capture many important physical details with

atomistic simulations, but modern computations are unable to reach the long relaxation

times observed for entangled system, which can reach minutes or hours. However, it

is possible to include entanglement effect in a soft-core model via slip-springs. In de
124 Chapter 16. NIST-CHiMaD Postdoctoral Research Fellows

Figure 16.1: Self-assembled coacervate complex in the center of the simulation box. Here soft-core

model, same concentrations of polymers and salt is used.

Pablo group the Theoretically Informed Entanglement Polymer Simulations Slip-Spring

model was proposed. Since I joined the group several important modifications were

made to the model, which allowed to avoid few issues of the previous implementation.

The modified model is applied to homopolymer linear and non-linear rheology and to

simulations of entangled diblock copolymers with great success. The ultimate goal of

my work on the model is enabling straight-forward mapping of parameters from detailed

(atomistic) simulations, while newly developed GPU algorithm provides a way to perform

simulation long enough to capture entanglement relaxation. The dynamic observables

soft-core slip-spring simulations are compared to the results of detailed simulations. The

detailed simulation data is provided by Douglas (NIST). The paper based on this work is

in preparation.
125

Nana Ofori-Opoku (NU/NIST)

Phase-Field Methods

Supervisors: Peter Voorhees (NU), James Warren (NIST)

Bio Dr. Ofori-Opoku holds a Ph.D. from the Materials Science and

Engineering Department, McMaster University. While completing

his Ph.D., he held multiple appointments as visiting researcher

at TKK Aalto University and VTT Technical Research Centre in

Finland. Before joining CHiMaD as a NIST-CHiMaD Postdoctoral Research Fellow, he was a postdoctoral fellowship at McGill

University working on recrystallization kinetics and microstructure development for nanoscale devices using continuum and

atomistic models. His research interests combine computational

materials science and condensed matter physics, with emphasis

on multi-scale modeling of non-equilibrium materials phenomena

and microstructure evolution.

Figure 16.2: Simulation of out of equilibrium solidification of a faceted crystal in the presence of

thermal fluctuations. The atomic probability density of the crystal lattice is shown. Clearly shown

is the ledge nucleation and growth mechanism, a

hallmark of faceted systems.

Research Developing a phase field model

of faceted eutectic growth to produce a predictive model for describing microstructure and spacing selection as a function of

the processing conditions. This is in collaboration with Voorhees (NU) and Warren (NIST). We have opted to use the more

atomistic phase field crystal methodology,

as describing faceted structures require

atomic scale resolution. In doing so, we

are now working on validating the phase

field crystal methodology, for single component systems, applied to faceted solidification structures. The model is currently

being tested and validated for its capabilities in quantitatively and accurately describing faceted microstructures and their

behavior. Thus far, the initial investigation is garnering insights into such behavior, e.g. surface energetics, growth modes,

and out-of-equilibrium kinetics. These insights are then going to be used, shortly,

in developing an alloy phase field crystal model for a faceted eutectic study.
126 Chapter 16. NIST-CHiMaD Postdoctoral Research Fellows

Fanny Rodolakis Simoes (ANL/NIST)

Directed Self-Assembly of Block Copolymers Films for Lithographic Applications

Supervisors: Paul Nealey (UC), Joseph Kline (NIST)

Bio Dr. Rodolakis Simoes received her Ph.D. in physics from

University Paris-Sud in 2009 for investigating the metal insulator

transitions in vanadium oxides combining different synchrotron

based X-ray techniques: photoemission, X-ray absorption and

inelastic scattering. During her post-doctoral experience at the University of Illinois in

Chicago and the Materials Science Division at ANL, she continued an interest in strongly

correlated systems using angle resolve photoemission spectroscopy (ARPES) to investigate

the Fermi surface collapse and hybridization effects in Ce-based heavy fermion compounds.

In 2014 she was awarded a NIST-CHiMaD postdoctoral research fellowship at the new

Intermediate Energy (250-2500 eV) X-ray beamline where she is now an assistant physicist.

This beamline will utilize resonant soft x-ray scattering (RSoXS) and ARPES to probe order

and disorder in emergent materials. Those materials include structural ordered systems

such as directed self-assembly of block copolymers and spin ices.

Figure 16.3: Specular (right) and 1st order (left)

diffraction peaks taken on a patterned polymer

sample infiltrated with aluminum oxide at the IEX

beamline

Research Dr. Rodolakis Simoes’ main

project was the commissioning of the new

Intermediate Energy X-ray (IEX) beamline at Sector 29 of the Advanced Photon Source (APS) at ANL, and in particular the development of a resonant soft xray scattering capability for directed selfassemblies block copolymer systems. She

performed first scattering experiment at

IEX, successfully testing the feasibility

of carrying out resonant soft X-ray scattering (RSoXs) on directed self-assembly

(DSA) block copolymer in collaboration

with Kline (NIST): Figure 16.3 shows the

very first diffraction pattern measured at the

IEX scattering end-station on a patterned

polymer sample infiltrated with aluminum

oxide using the delay-line microchannel

plate detector. The fact that both the specular and the first order diffraction peak can

be seen proves that the detector has the required dynamic range for the study of buried structure of DSA. She also collaborated with

Kline (NIST) to implement on the fly RSoXS data analysis tools at the IEX beamline. She

rebuilt and optimized the scattering in-vacuum Kappa diffractometer at the IEX beamline,

minimizing the sphere of confusion, improving robustness and adding limit switches to

avoid crashes. Designed and commissioned a new sample stage allowing measurements in

transmission. She continued the on-going commissioning of the beamline to fully characterized the x-ray beam at the sample. Developed user interfaces tools for the beamline and

the endstation. As a new beamline scientist at IEX, she plans to continue collaborating

with Nealey (UC) group to make IEX a local destination for RSoXS on DSA.
127

Peisheng Wang (NU/NIST)

Precipitation Strengthened Alloys

Supervisors: Gregory Olson (NU), Ursula Kattner (NIST), Carelyn

Campbell (NIST), Eric Lass (NIST)

Bio Dr. Wang’s main research interest lies in thermodynamic calculations (CALPHAD). He received his Ph.D. in 2012 from Central

South University, China for experimental determination of Mg phase

diagram and thermodynamic calculations. He was then a postdoctoral

researcher at TU Clausthal, Germany between 2012-2014. During his postdoctoral work

he was interested in Heterogeneous Nucleation and Microstructure Formation of Al-CuNi alloys and a new function to describe the Gibbs energy of liquid phase. In 2015 he

was awareded a NIST-CHiMaD Postdoctoral Research Fellowship to futher CALPHAD

methods under Precipitation-Strengthened Alloys use-case group.

Figure 16.4: Calculated CALPHAD enthalpy

of formation of the μ phase compared to firstprinciples (FLAPW) results. The reference state

is Hcp Co (non-magnetic) and Bcc W. The open

blue circles are the calculated enthalpy of formation for all the end-members of μ phase. The composition of each end-member is presented by a

five-digit number like 22122, where 1 corresponds

to Co and 2 to W, and the order of digits reflect

the occupancy of the corresponding sublattices in

the following order: Co1(3a)-W1(6c1)-W2(6c2)-

W3(6c3)-Co2(18h). For example, 12221 corresponds to the nominal μ-phase Co7W, 22221 -

the "new" μ-phase Co6W7. The error bar for

the FLAPW results is ±3kJ/mole-atoms (or 0.03

eV/mole-atoms).

Research Dr. Wang is working to establish a multicomponent thermodynamic

database for the Co-based superalloy; his

prime focus is the Co-Al-W-Ta-Ti-Ta system. Applications of traditional Co-based

alloys suffer from lower high-temperature

strength compared to Ni-base alloys. The

L12 γ

0 phase Co3(Al,W), exhibit a considerable improvement in high-temperature

strength, indicating a promising future for

the Co superalloys. However, consequent

experiments by Lass (NIST) show that the

ternary γ

0 phase Co3(Al,W) is a thermodynamically unstable phase, unless additional elements are added for stabilizing

the γ

0 phase. Therefore, a multicomponent

Co database with rigorous thermodynamics description of phase stability of the γ

0

phase can facilitate Co-Al-W based superalloy design. Dr. Wang’s main accomplishments in CHiMaD Year Two are: (1) The

Co-Al, Co-W, Co-Ta, Al-W and Al-Ni systems are critically reviewed and reassessed

by cooperating density functional theory

(DFT) calculations. The critical γ

0 phase

was described as metastable phase in CoAl, Co-W and Al-W systems; (2) A thermodynamic database for Co-Al-W-Ni-TiTa system including all the binaries is now

deliverable. More complex and accurate

sublattice models were used in the present database compared to the other available

multi-component database. For example, four sublattice order-disorder transition models have been used to describe both B2 and L12 structures. One of the representative

accomplishments is the optimization for the Co-W system, as shown in Figures 16.4.

17. CHiMaD Principal Investigators and Staff

17.1 CHiMaD Principal Investigators

Peter Voorhees

CHiMaD Co-Director

Frank C. Engelhart Professor of Materials Science and Engineering

Department of Materials Science and Engineering

Northwestern University

Gregory Olson

CHiMaD Co-Director

Walter P. Murphy Professor of Materials Science and Engineering

Department of Materials Science and Engineering

Northwestern University

Juan De Pablo

CHiMaD Co-Director

Liew Family Professor in Molecular Engineering

Institute for Molecular Engineering

University of Chicago

Ankit Agrawal

Research Associate Professor

Department of Electrical Engineering and Computer Science

Northwestern University

Micheal Bedyzk

Department Chair and Professor of Materials Science and Engineering

Department of Materials Science and Engineering

Northwestern University

Larry Berardinis

Technology Projects Manager, Computational Materials Data (CMD) Network

ASM International

Ida Berglund

Materials Design Engineer
130 Chapter 17. CHiMaD Principal Investigators and Staff

QuesTek Innovations LLC

Catherine Brinson

Jerome B. Cohen Professor of Mechanical Engineering

Associate Dean for Academic and Professional Initiatives

Department of Mechanical Engineering

Northwestern University

Jian Cao

Associate Vice President of Research

Director of Northwestern Initiative for Manufacturing Science and Innovation (NIMSI)

Professor of Mechnical Engineering

Department of Mechanical Engineering

Northwestern University

Wei Chen

Wilson-Cook Professor in Engineering Design

Professor of Mechanical Engineering

Department of Mechanical Engineering

Northwestern University

Alok Choudhary

Henry Isabelle Dever Professor of Electrical Engineering and Computer Science

Department of Electrical Engineering and Computer Science

Northwestern University

Yip-Wah Chung

Professor of Materials Science and Engineering

Department of Materials Science and Engineering

Northwestern University

Ian Foster

Director, Computation Institute

Professor, Department of Computer Science

Senior Scientist, Mathematics and Computer Science Division

University of Chicago

Argonne National Laboratory

Giulia Galli

Liew Family Professor in Molecular Engineering

Institute for Molecular Engineering

University of Chicago

Olle Heinonen

Strategic Initiative Lead in Computational Chemistry and Materials

Materials Scientist

Fellow, Northwestern-Argonne Institute of Science and Technology

Materials Science and Engineering Division

Argonne National Laboratory

Scott Henry

Director, Content & Knowledge-Based Solutions

ASM International
17.1 CHiMaD Principal Investigators 131

Mark Hersam

Walter P. Murphy Professor of Materials Science and Engineering

Director, Materials Research Science and Engineering Center (MRSEC)

Department of Materials Science and Engineering

Northwestern University

Heinrich M. Jaeger

William J. Friedman and Alicia Townsend Professor of Physics

James Franck Institute and Department of Physics

University of Chicago

Dmitry Karpeev

Assitant Computational Mathematician

Fellow, Computation Institute

Mathematics and Computer Science Division

Argonne National Laboratory

Sinan Keten

Assistant Professor of Mechanical Engineering

Assistant Professor of Civil and Environmental Engineering

Department of Mechanical Engineering

Department of Civil and Environmental Engineering

Northwestern University

Lincoln Lauhon

Professor of Materials Science and Engineering

Department of Materials Science and Engineering

Northwestern University

Wing-Kam Liu

Walter P. Murphy Professor of Mechanical Engineering & Civil and Environmental

Engineering

Department of Mechanical Engineering

Department of Civil and Environmental Engineering

Northwestern University

Erik Luijten

Professor of Materials Science and Engineering

Department of Materials Science and Engineering

Engineering Sciences and Applied Mathematics

Northwestern University

Tobin J. Marks

Charles E. and Emma H. Morrison Professor of Chemistry

Professor of Materials Science and Engineering

Vladimir N Ipatieff Professor of Catalytic Chemistry

Department of Chemistry

Northwestern University

Sid Nagel

Stein-Freiler Distinguished Service Professor

James Franck Institute and Department of Physics

University of Chicago
132 Chapter 17. CHiMaD Principal Investigators and Staff

Paul Nealey

Brady W. Dougan Professor in Molecular Engineering

Institute for Molecular Engineering

University of Chicago

James Saal

Sr. Materials Design Engineer

QuesTek Innovations LLC

Jason Sebastian

Director of Technology

QuesTek Innovations LLC

Steven Sibener

Carl William Eisendrath Distinguished Service Professor

Department of Chemistry and The James Frank Institute

University of Chicago

Barry Smith

Senior Computational Mathematician

Senior Fellow, Northwestern-Argonne Institute of Science and Technology

Mathematics and Computer Science Division

Argonne National Laboratory

David Snyder

Sr. Materials Development Engineer

QuesTek Innovations LLC

Matthew Tirrell

Dean and Founding Pritzker Director

Institute for Molecular Engineering

University of Chicago

Monica Olvera de la Cruz

Lawyer Taylor Professor of Materials Science and Engineering

Department of Materials Science and Engineering

Northwestern University

Alexander Umantsev

Professor of Physics

Department of Chemistry and Physics

Fayetteville State University

Christopher Wolverton

Professor of Materials Science and Engineering

Department of Materials Science and Engineering

Northwestern University

Luping Yu

Professor of Chemistry

Department of Chemistry

University of Chicago
17.2 CHiMaD Technical Staff 133

17.2 CHiMaD Technical Staff

Emine Begum Gulsoy

CHiMaD Associate Director

Fellow, Northwestern-Argonne Institute of Science and Engineering

Research Associate

Department of Materials Science and Engineering

Northwestern University

Laura Bartolo

CHiMaD Data & Database Efforts Coordinator

Northwestern-Argonne Institute of Science and Engineering

Northwestern University

Ricardo Komai

CHiMaD Materials Design Expert

Postdoctoral Researcher

Department of Materials Science and Engineering

Northwestern University

17.3 CHiMaD Administrative Staff

Goldie Gorski

Director of Operations and Outreach

Northwestern-Argonne Institute of Science and Engineering

Northwestern University

Deneen Marie Bryce

Business Administrator

Northwestern-Argonne Institute of Science and Engineering

Northwestern University

Janet Boland

Executive Assistant

Institute for Molecular Engineering

University of Chicago

Kathleen Bennett

Program Assistant

Northwestern-Argonne Institute of Science and Engineering

Northwestern University

18. CHiMaD NIST Collaborators

James Warren

Techincal Program Director, Materials Genomics

Materials Measurement Laboratory

NIST, Gaithersburg, MD

Debra Audus

Chemical Engineer, Polymers and Complex Fluids Group

Materials Science and Engineering Division

Materials Measurement Laboratory

NIST, Gaithersburg, MD

Chandler Becker

Materials Research Engineer, Thermodynamics and Kinetics Group

Materials Science and Engineering Division

Materials Measurement Laboratory

NIST, Gaithersburg, MD

Mary Brady

Group Leader, Information Systems Group

Software and Systems Division

Information Technology Laboratory

NIST, Gaithersburg, MD

Benjamin Burton

Research Scientist, Thermodynamics and Kinetics Group

Materials Science and Engineering Division

Materials Measurement Laboratory

NIST, Gaithersburg, MD

Carelyn Campbell

Group Leader, Thermodynamics and Kinetics Group

Materials Science and Engineering Division

Materials Measurement Laboratory

NIST, Gaithersburg, MD

Chelsea Davis
136 Chapter 18. CHiMaD NIST Collaborators

NRC Postdoctoral Fellow, Polymers and Complex Fluids Group

Materials Science and Engineering Division

Materials Measurement Laboratory

NIST, Gaithersburg, MD

Albert Davydov

Leader, Functional Nanostructured Materials Group

Materials Science and Engineering Division

Materials Measurement Laboratory

NIST, Gaithersburg, MD

Alden Dima

Research Scientist

Software and Systems Division

Information Technology Laboratory

NIST, Gaithersburg, MD

Jack Douglas

NIST Fellow

Materials Science and Engineering Division

Materials Measurement Laboratory

NIST, Gaithersburg, MD

Aaron Foster

Materials Research Engineer, Polymeric Materials Group

Materials and Structural Systems Division

Engineering Laboratory

NIST, Gaithersburg, MD

Douglas Fox

Guest Researcher, Flammability Reduction Group

Fire Research Division

Engineering Laboratory

NIST, Gaithersburg, MD

Jeffrey Gilman

Polymer Chemist, Polymers and Complex Fluids Group

Materials Science and Engineering Division

Materials Measurement Laboratory

NIST, Gaithersburg, MD

Jonathan Guyer

Group Leader, Mechanical Performance Group

Materials Science and Engineering Division

Materials Measurement Laboratory

NIST, Gaithersburg, MD

Robert Hanish

Director

Office of Data and Informatics

Materials Measurement Laboratory

NIST, Gaithersburg, MD
137

Adam Hannon

NRC Postdoctoral Fellow, Functional Polymers Group

Materials Science and Engineering Division

Materials Measurement Laboratory

NIST, Gaithersburg, MD

Ursula Kattner

Physical Scientist, Thermodynamics and Kinetics Group

Materials Science and Engineering Division

Materials Measurement Laboratory

NIST, Gaithersburg, MD

Ketan Khare

Postdoctoral Researcher, Polymers and Complex Fluids Group

Materials Science and Engineering Division

Materials Measurement Laboratory

NIST, Gaithersburg, MD

Joseph Kline

Project Leader, Functional Polymers Group

Materials Science and Engineering Division

Materials Measurement Laboratory

NIST, Gaithersburg, MD

Kenneth Kroenlein

Director, Thermodynamic Research Center

Applied Chemicals and Materials Division

NIST, Boulder, CO

Sergiy Krylyuk

Guest Researcher, Functional Nanostructured Materials Group

Materials Science and Engineering Division

Materials Measurement Laboratory

NIST, Gaithersburg, MD

Eric Lass

Materials Research Engineer, Thermodynamics and Kinetics Group

Materials Science and Engineering Division

Materials Measurement Laboratory

NIST, Gaithersburg, MD

Lyle Levine

Project Leader, Additive Manufacturing of Metals

Materials Science and Engineering Division

Materials Measurement Laboratory

NIST, Gaithersburg, MD

Alexander Liddle

Deputy Director (Acting) and Group Leader

Nanofabrication Research Group

Center for Nanoscale Science and Technology

NIST, Gaithersburg, MD
138 Chapter 18. CHiMaD NIST Collaborators

Li Ma

Guest Scientist, Mechanical Performance Group

Materials Science and Engineering Division

Materials Measurement Laboratory

NIST, Gaithersburg, MD

Kil-Won Moon

Postdoctoral Researcher, Thermodynamics and Kinetics Group

Materials Science and Engineering Division

Materials Measurement Laboratory

NIST, Gaithersburg, MD

Sarala Padi

Research Scientist

Software and Systems Division

Information Technology Laboratory

NIST, Gaithersburg, MD

Adele Peskin

Research Scientist

Software and Systems Division

Information Technology Laboratory

NIST, Gaithersburg, MD

Frederick Phelan

Chemical Engineer, Polymers and Complex Fluids Group

Materials Science and Engineering Division

Materials Measurement Laboratory

NIST, Gaithersburg, MD

Raymond Plante

Research Scientist

Materials Science and Engineering Division

Materials Measurement Laboratory

NIST, Gaithersburg, MD

Vivek Prabhu

Chemical Engineer, Polymers and Complex Fluids Group

Materials Science and Engineering Division

Materials Measurement Laboratory

NIST, Gaithersburg, MD

Lee Richter

Physicist, Functional Polymers Group

Materials Science and Engineering Division

Materials Measurement Laboratory

NIST, Gaithersburg, MD

Deepak Sharma

Guest Researcher, Functional Nanostructured Materials Group

Materials Science and Engineering Division

Materials Measurement Laboratory

NIST, Gaithersburg, MD
139

Arunima Singh

Guest Researcher, Thermodynamics and Kinetics Group

Materials Science and Engineering Division

Materials Measurement Laboratory

NIST, Gaithersburg, MD

Mark Stoudt

Materials Research Engineer

Materials Science and Engineering Division

Materials Measurement Laboratory

NIST, Gaithersburg, MD

Francesca Tavazza

Research Scientist, Thermodynamics and Kinetics Group

Materials Science and Engineering Division

Materials Measurement Laboratory

NIST, Gaithersburg, MD

Maureen Williams

Materials Scientist, Thermodynamics and Kinetics Group

Materials Science and Engineering Division

Materials Measurement Laboratory

NIST, Gaithersburg, MD

Jonathan Winterstein

Research Scientist, Nanofabrication Research Group

Center for Nanoscale Science and Technology

NIST, Gaithersburg, MD

Jeremiah Woodcock

Research Chemist, Polymers and Complex Fluids Group

Materials Science and Engineering Division

Materials Measurement Laboratory

NIST, Gaithersburg, MD

Sharief Youssef

Research Scientist

Software and Systems Division

Information Technology Laboratory

NIST, Gaithersburg, MD

19. CHiMaD Outreach

When Materials Genome Initiative was put forth by the White House in 2011, the three pillars of the initiative were described as: (1) Developing a open-access and open-source materials innovation infrastructure, (2) Achieving national goals with advanced materials using

this infrastructure, (3) Equipping the next generation materials workforce in government,

academia and industry. [https://www.whitehouse.gov/sites/default/files/microsites/ostp/

materials_genome_initiative-final.pdf]. CHiMaD workshops are designed and organized

with these three major goals of the MGI in mind with an emphasis on training the current

and next generation of government, industrial and academic researchers with the right

tools to make MGI a sustainable success.

Towards this goal, in 2015, CHiMaD organized an MGI Seminar series, welcoming an

international group of researchers to present about their MGI-related efforts as well as other

international MGI initiatives they are a part of. This series of presentations were broadcast

to all CHiMaD partner institutions (Section 19.1). In accordance with the goal of equipping

the next generation of researcher and in collaboration with ASM International Educational

Committee, CHiMaD initiated the dissemination of some of the most significant current

materials design software to engineering universities in the U.S. where the universities

are expected to build these tools into their undergraduate curriculum. In its first round,

proposals by six universities were awarded (Section 19.2). CHiMaD also took the lead

in the organization of various high-impact workshops aiming at community building and

bridging for collaborations and open-access exchange of knowledge on their topics of

interest (Section 19.5). Finally, CHiMaD in 2015 has been actively working on improving

its outreach activities for under represented groups (Section 19.4). This chapter, therefore,

provides an overview of the CHiMaD Outreach activities in 2015.

19.1 MGI Seminar Series

All CHiMaD seminar abstracts, presenter biographies and event fliers are available on the

CHiMaD Event Archives at http://chimad.northwestern.edu/news-events/Event_Archives.html

03/24/15 - CHiMaD HQ & Broadcast

TMS Initiatives Related to ICME/MGI, Manufacturing, and Energy
142 Chapter 19. CHiMaD Outreach

George Spanos

Technical Director, TMS

04/30/15 - CHiMaD HQ & Broadcast

Low-Dimensional Semiconductors for Electronics, Sensors, and Energy

Albert Davydov

Group Leader, Functional Materials Group

National Institute of Standards and Industry

04/30/15 - CHiMaD HQ & Broadcast

TCombining X-Ray Scattering and Materials Modeling for Directed Self-Assembly Morphology Measurements

Joseph Kline

Project Leader, Functional Polymers Group

National Institute of Standards and Industry

06/23/15 - CHiMaD HQ & Broadcast

Elucidating the Role of Boron in Extending Persistence Eu2+ and Dy3+ Co-doped Sr4Al14O25

by Cathodoluminescence, STEM-EDX and Electron Diffraction

Cleva Ow-Yang

Professor, Department of Materials Science and Engineering

Sabanci University, TURKEY

06/25/15 - CHiMaD HQ & Broadcast

Bio-inspired Self-healing Ceramics for Turbine Blade Applications

Toshio Osada

Researcher, Structural Materials Unit

National Institute for Materials Science (NIMS), JAPAN

07/27/15 - Northwestern University

From Atoms to Alloy Engineering Behaviour

David Dye

Professor, Department of Materials Science and Engineering

Imperial College London, ENGLAND

07/31/15 - CHiMaD HQ & Broadcast

Multi-scale Simulation of Steel and Aluminum Alloy Sheets using Phase Field and Crystal

Plasticity Finite Element Methods: From Microstructure to Sheet Metal Forming

Akinori Yamanaka

Assistant Professor, Department of Mechanical Systems Engineering

Tokyo University of Agriculture and Technology, JAPAN

08/19/15 - CHiMaD HQ & Broadcast

Multi-scale Finite Element Modeling in Structural Metals

Ikumu Watanabe

Senior Researcher

National Institute for Materials Science (NIMS), JAPAN
19.1 MGI Seminar Series 143

09/17/15 - CHiMaD HQ & Broadcast

Studying the Micromechanics of Marthensitic Phase Transformations Using High Energy

Diffraction Microscopy

Aaron Stebner

Assistant Professor, Department of Materials Science and Engineering

Colorado School of Mines

09/21/15 - CHiMaD HQ & Broadcast

Electrochemistry on Nano- and Atomic Levels: Scanning Probe Microscopy Meets Deep

Data

Sergei V. Kalinin

Director, Institute for Functional Imaging of Materials

Distinguished Research Staff Member, Center for Nanophase Materials Sciences

Oak Ridge National Laboratory

09/22/15 - Argonne National Laboratory

Big, Deep, and Smart Data in Energy Materials Research: Atomic View on Materials

Functionalities

Sergei V. Kalinin

Director, Institute for Functional Imaging of Materials

Distinguished Research Staff Member, Center for Nanophase Materials Sciences

Oak Ridge National Laboratory

10/08/15 - Fayetteville State University

Atomistic Scale Modeling of Faceted Solidification and Microstructure Evolution

Nana Ofori-Opoku

NIST-CHiMaD Postdoctoral Research Fellow

Center for Hierarchical Materials Design

National Institute of Standards and Industry

11/11/15 - CHiMaD HQ & Broadcast

Vacuum Induction Melting and Vacuum Arc Remelting of Co-Al-W-X Gamma-Prime

Superalloys

Erin T. McDevitt

Manager, Research and Development

ATI Specialty Materials

12/09/15 - CHiMaD HQ & Broadcast

NIMS Materials Database: Current Status and Future Prospects

Yibin Xu

Leader, Materials Database Group

National Institute for Materials Science (NIMS), JAPAN

12/14/15 - CHiMaD HQ & Broadcast

Towards Predictive ab initio Simulation of Surfaces and Interfaces

Ikutaro Hamada

MANA Researcher, International Center for Materials Nanoarchitectronics

National Institute for Materials Science (NIMS), JAPAN
144 Chapter 19. CHiMaD Outreach

19.2 ASM Materials Genome Toolkit

ASM MGI Toolkit Awardees

California State Polytechnic

University in Pomona

Lead PI: Vilupanur Ravi

Chemical and Materials Engineering

*1st Place, 2015 ASM Undergraduate

Design Competition

Carnegie Mellon University

Lead PI: Bryan Webler

Department of Materials Science and Engineering

*2nd Place, 2015 ASM Undergraduate

Design Competition

Michigan Technological Institute

(Pilot Program)

Lead PI: Paul Sanders

Department of Materials Science and Engineering

*3rd Place, 2015 ASM Undergraduate

Design Competition

Alfred University

Lead PI: S. K. Sundaram

Department of Materials Science and Engineering

University of Maryland

Lead PI: Yifei Mo

Department of Materials Science and Engineering

Virginia Polytechnic Institute and

State University

Lead PI: Alan Druschitz

Department of Materials Science and Engineering

Colorado School of Mines

(Awarded through LIFT)

Lead PI: Kip Findley

Department of Metallurgical and Materials Engineering

ASM International and its affiliate members administered a competition among

undergraduate universities in the U.S.,

awarding six schools with state-of-theart materials design software and related

materials property databases. The competition was announced in early 2015

and broadly publicized via direct email,

on the ASM and CMD Network websites, through several ASM newsletters,

and in a press release issued to various

news outlets and materials-oriented professional societies. Schools competed

on the basis of a proposal stating how

they planned to integrate the Materials

Genome Toolkit software into their undergraduate curriculum and in the context of the annual ASM Undergraduate

Design Competition. A review committee convened by the ASM Action in Education Committee, the subcommittee

on Computational Materials Engineering, evaluated the entries, ranking them

according to several factors, including

the degree to which the proposed plan

balanced theory and application in the

use of the software.

In the first round of the competition,

six engineering schools were selected

to receive the Toolkit, including Alfred

University, California State Polytechnic

University in Pomona, Carnegie Mellon

University, Michigan Technological Institute, the University of Maryland, and

Virginia Polytechnic Institute and State

University. Each school now has a threeyear, multi-user license, giving hundreds of students and faculty across the

U.S. access to the latest Thermo-Calc

thermodynamics code, DICTRA multicomponent diffusion code, TC-PRISMA

precipitation simulator, and several thermodynamic and mobility databases. An

equivalent software package has also

been awarded to the Colorado School of Mines, one of the top seven finishers in the

toolkit competition, under the auspices of the Lightweight Innovations for Tomorrow
19.2 ASM Materials Genome Toolkit 145

(LIFT) consortium.

The CHiMaD-sponsored ASM Materials Genome Toolkit program is made possible by a

NIST grant, the ASM Materials Education Foundation, and Thermo-Calc Software, which

generously offered an 85% discount on what will ultimately be twelve sets of tools worth

more than $1.6 million. The software distribution program addresses several CHiMaD

objectives by helping the next generation of scientists and engineers become proficient with

materials-by-design tools and techniques and by contributing to efforts that are advancing

the collective knowledge on material systems and methodologies.

ASM is conducting a second Materials Genome Toolkit competition in the coming year that

will award four more engineering schools with state-of-the-art materials design software.

The application deadline for the proposals is April 30, 2016. As in the first round of

the competition, ASM is promoting the contest to faculty and students through email,

newsletters, web posts, community forums, AM&P magazine, and a broadcast press

release. ASM will also publicly recognize the first ten MGI toolkit winners during an

ASM function at Materials Science and Technology 2016 in Salt Lake City, Utah, and will

work to develop metrics to capture and convey how the toolkit program is benefitting each

school.

ASM Principal Investigators gave 9 presentations regarding the CHiMaD-ASM outreach

activities in 2015 (see section 21.13).
146 Chapter 19. CHiMaD Outreach

19.3 Workshops and Meetings

The agendas for CHiMaD-organized and supported workshops are available on the CHiMaD Event Archives at http://chimad.northwestern.edu/news-events/Event_Archives.html

Phase Field Methods Workshop I

January 9, 2015 | Northwestern University

Phase Field Methods Hackathon

October 14, 2015 | CHiMaD Headquarters

Phase Field Methods Workshop II

October 15-16, 2015 | CHiMaD Headquarters

Organizers: Olle Heinonen (ANL), Peter Voorhees (NU)

Attendees of the CHiMaD Phase Field Methods Workshop II & Hackathon

Scope On January 9th, 2015, CHiMaD hosted a small workshop focused on starting a

discussion regarding the development of a community standard phase field code, and to

address how to include the varying needs of the community within these codes. This

invitation-only workshop consisted of 18 world-leading experts and framework developers

on phase field methods. A seed group lead by Olle Heinonen (ANL) and consisting of

Peter Voorhees (NU), James Warren (NIST) and Jonathan Guyer (NIST) were nominated

to determine a set of community benchmark problems over which the results of different

phase field codes and frameworks from around the world could be directly compared.

A draft of these benchmark problems were created between January and October. On

October 14, 2015 CHiMaD organized the CHiMaD Phase Field Methods Hackathon where

teams of two students and postdoctoral researchers representing the major phase field

frameworks were asked to solve these benchmark problems. This hackathon allowed for

the first direct comparison of codes and the results were presented and discussed during the
19.3 Workshops and Meetings 147

CHiMaD Phase Field Methods Workshop II on October 15-16,2015. As a result of these

discussions, the benchmark were improved. To continue the discussion, a third workshop

is being planned for 2016, along with another hackathon.

31st Steel Research Group (SRG) Meeting

March 23-24, 2015 | Northwestern University

NU-NIMS for Materials Innovation: Joint MGI Workshop

March 25, 2015 | Northwestern University

Organizer: Gregory Olson (NU)

Scope Northwestern’s Steel Research Group (SRG) materials design consortium joined

the CHiMaD umbrella organization during Year One, offering the leveraging opportunity

of the CALPHAD-based metals design projects with topics and sponsors listed in Figure

19.1.

Figure 19.1: SRG Materials Design Projects

Of particular note in 2015 was

the fragment penetration testing

of the ONR Cyberalloys 2020

prototype austenitic blast protection TRIP steel. Under optimized transformation stability,

numerous arrested shear bands

were found with clear transformation zones at their tips,

supporting the effectiveness of

transformation plasticity in inhibiting adiabatic shear localization. The collaboration with GM

under the DOE Lightweighting

Initiative has quantified the thermodynamics and kinetics of Q

phase precipitation strengthening in support of cast Al alloys with higher temperature performance. Caterpillar collaboration under the same DOE initiative has adapted multiphase precipitation concepts

from our previous martensitic blast protection steel designs to demonstrate novel cast steel

prototypes for lower cost crankshaft applications. AHSS design research supported by

ArcelorMittal is quantifying the nonequilibrium thermodynamics of carbon partitioning

in Quench & Partition martensite-austenite TRIP steels for high formability automotive

sheet applications. In support of the new process technology of additive manufacturing,

the NIST-MSAM project has quantified recrystallization behavior of Ti and steel alloys,

while QuesTek’s collaboration with Honeywell under the DARPA Open Manufacturing initiative is supporting AIM qualification of printed Ni superalloy components for aerospace

applications. Building on our CHiMaD Year One demonstration of a hybrid approach

to integrating CALPHAD with datamining techniques, QuesTek has secured a $3.6M

consortium project with CHiMaD to apply this approach to integrated compound discovery

and predictive microstructural design in thermoelectric materials.

These projects will help demonstrate the generality of the methods, tools, and database

infrastructure of CHiMaD. The SRG Annual Meeting held each March at Northwestern
148 Chapter 19. CHiMaD Outreach

will continue to serve as a review of CHiMaD metals activities offering interaction with a

broader community.

CHiMaD Annual Review Meeting

May 1, 2015 | Northwestern University

Peter Voorhees presenting the overview of CHiMaD activities for 2014

Scope The Center’s 2015 Annual Meeting brought together 80 CHiMaD principal investigators and collaborators from CHiMaD’s partner institutions, including ASM International,

QuesTek Innovations and Fayetteville State University, as well as NIST colleagues to

review the CHiMaD research and outreach activities. This meeting also acts as a platform

for the CHiMaD technical advisory board to review the CHiMaD research activities and

provide feedback.

CHiMaD also organized its first poster session during 2015 Annual Review Meeting giving

the students and postdoctoral researchers an opportunity to explain their contributions to

CHiMaD research and get feedback regarding their work directly from CHiMaD principal

investigators, NIST colleagues and the Technical Advisory Board. In this poster session, 25

Posters were presented representing all use-case groups, tools and the educational outreach

activities.

Roadmapping for Innovation in Sheet Metal Forming Workshop

May 4-5, 2015 | Northwestern University

Organizer: Jian Cao (NU)

Scope This workshop brought together a select group of key academic and industry leaders

to discuss and identify critical gaps, technology advancements, and innovations that could

transform sheet metal forming. The program will launch a roadmapping process to guide

and stimulate action to enhance the industry and U.S. competitiveness. More information

can be found at http://sheetmetal-roadmap.northwestern.edu.

NIST Diffusion/CALPHAD Data Informatics and Tools Workshop

May 14-15, 2015 | National Institute of Standards and Technology
19.3 Workshops and Meetings 149

Organizers: Carelyn Campbell (NIST)

Scope This workshop hosted a series of presentations discussing the current major data

informatics efforts as well as those at NIST. A complete list of talks can be found at

http://www.nist.gov/mml/msed/thermodynamics_kinetics/Diffusion-Workshop-Group.cfm

International Workshop on Advanced Co-based Superalloys: 3.0

June 23-24, 2015 | National Institute of Standards and Technology

Organizers: Eric Lass (NIST), Steffen Neumeier (FAU Erlangen-Nurnberg, Germany),

Suzana Fries (RUB, Bochum, Germany)

Scope This workshop brought together an international group of researcher focusing on

Co-based superalloys for 2 days of discussions and poster session.

US-Japan Materials Genome Workshop

June 23-24, 2015 | International Congress Center "Epochal Tsukuba" (JAPAN)

Plenary speakers included: James Warren (NIST), Gregory Olson (NU)

Scope Structural materials play a central role in National physical infrastructure development, and both the US and Japan have concerted efforts in developing structural materials

for applications in maintaining, and improving their large-scale physical infrastructure.

In addition, both nations have efforts to discover, design, and deploy advanced structural

materials for dynamic applications as in aerospace and power generation industries. These

are major undertakings that require a concerted effort ranging from fundamental materials

science research, to acquiring, curating, and maintaining high-quality data, and, ultimately,

to manufacturing products that are energy efficient, environmentally sustainable, and

durable. The goals of this and subsequent workshops is to bring together a diverse group of

researchers from the US and Japan to discuss ways to use predictive theory and modeling,

combined with machine learning, data mining, and rapid-acquisition of experimental data

to produce highly efficient and low-cost manufactured products.

This workshop strives to develop a collaborative framework for complementary joint

research using the materials genome and concurrent engineering paradigm to meet the

development goals set by each country. To this end, a 2-day agenda with 5 plenary

speakers and 30 distinguished colleagues in the field to lecture and lead 5 parallel sessions

were designed. The workshop helped identify both important scientific and engineering

challenges in materials topics including metals, polymer composites, and ceramics.

CHiMaD Materials Design Workshop I

September 24-25, 2015 | CHiMaD Headquarters

Organizers: Ricardo Komai (NU), Wei Xiong (NU), Begum Gulsoy (NU)

Scope This workshop was organized to further introduce Materials by Design concepts

to CHiMaD graduate students and postdoctoral researchers. The goal of this two-day

community-building workshop was to form a Systems Design Chart for individual CHiMaD

projects. This effort was very beneficial for the students and postdocs in furthering their

understanding of Materials by Design and for identifying the unique materials design

goals for each use-case group and its projects. Sauza (NU), Gross (NU) and Peters (NU)

assisted with this course, providing leading examples of building systems design charts for
150 Chapter 19. CHiMaD Outreach

Attendees of the CHiMaD Materials Design Workshop I

their projects. The discussions during the workshop gave rise to a possible collaboration

between the Data Mining and Organic Bulk Heterogeneous Polymer Solar Cells use-case

groups. The students and postdocs are expected to further improve and refine the systems

design chart as their projects progress. The workshop report will be made available and a

follow-up workshop is planned for 2016.

Scientific Symposium on

Multivalent Interactions in Polyelectrolytes: New Physics, Biology and Materials

October 2-4, 2015 | University of Chicago

Organizers: Juan de Pablo (UC), Sheng-Lin Gibson (NIST), Monica Olvera de la Cruz

(NU), Vivek Prabhu (NIST), Matthew Tirrell (UC)

Attendees of the Scientific Symposium on Multivalent Interactions in Polyelectrolytes
19.3 Workshops and Meetings 151

Scope This symposium explored the new physical, biological and materials science possible

when highly charged macromolecules interact with multivalent species of the opposite

charge. Important new structures, ranging from encapsulants to ordered block copolymer

phases to stress granules in biology, result from multivalent electrostatic assembly. Major

opportunities in this field include: understanding counterion condensation with multivalent

ions and its role in macromolecular collapse; understanding why some polyelectrolyte

complexes are solid and some are fluid; design of new self-assembled structures via

electrostatic complexation; specific ion effects; understanding the structure and function of

membrane-less granules and compartments in biology.

About 100 researchers attended the symposium where 40 experts from around the world

gave invited presentations regarding their work in the field. Matthew Tirrell’s accomplishments in this field were recognized with a reception and dinner during the event. This

symposium was mainly organized by the members of the Soft Matter Design Based on

Charge Complexation use-case group.

Advances and Challenges in Soft Matter Photovoltaic Research

Novermber 13-14, 2015 | University of Chicago

Organizers: Luping Yu (UC), Tobin Marks (NU), Lee J. Richter (NIST)

Scope The aim of this symposium was to discuss recent advances, and to define what are

viewed as the grand challenges for development of organic solar cells. A central aspect

of the symposium was to examine emerging tools, particularly predictive computational

methods and data-base related strategies, and their role in advancing OPV science and

related areas (e.g., organic electronics). This symposium also served as a platform to exchange new ideas, explore common challenges, and forge cross-disciplinary collaborations.

Especially useful were the discussions of solutions to critical issues we are facing in OPV

area, such as low power conversion efficiency, inadequate carrier mobility, Voc losses,

long term stability, theory-guided design of materials, and charge generation dynamics, to

name a few. About 40 researchers attended the symposium where 10 experts gave invited

presentations. This symposium was mainly organized by the members of the Organic Bulk

Heterojunction Polymer Solar Cells use-case group.

Semi-Automated Data Base Creation Course, University of Chicago

Organizers: Juan de Pablo (UC), Debra Audus (NIST), Ian Foster (UC/ANL)

Attendees of the Semi-Automated Data Base Creation Course

The organizers have developed a model for semi-automated data base creation. The
152 Chapter 19. CHiMaD Outreach

current process now involves a verification step, which was implemented by teaching an

undergraduate course at the University of Chicago during which students were asked to

check the relevance of automated data entries into the data base. Audus (NIST) spent three

weeks at Chicago collaborating on this project and co-teaching that course. A manuscript

describing this project has been submitted for publication in the Journal of Chemical

Education.

19.4 Outreach to Under Represented Groups

In Spring Quarter of 2015, Materials Design (MAT 390) course taught by Gregory Olson

at Northwestern University was simultaneously broadcast to Fayetteville State University

(FSU). Lead by Umantsev, two students and two faculty attended the lectures and received

a copy of the class reading material to expand their knowledge of materials design. As a

continuation of this effort a special agreement was negotiated with ThermoCalc to provide

an additional 3-year license to the Materials Genome Toolkit, disseminated by ASM

outreach efforts, to FSU at no cost.

On October 8, 2015, CHiMaD-NIST Postdoctoral Researcher Nana Ofori-Opoku visited

FSU, hosted by Umantsev, to deliver a CHiMaD seminar on Atomistic Scale Modeling of

Faceted Solidification and Microstructure Evolution. During this time he also introduced a

diversity-focused CHiMaD summer internship opportunity under the Northwestern MRSEC REU Program for Summer 2016 to the students and faculty present. This opportunity,

created after discussions with Umantsev will involve a project that has a strong experimental aspect and aims to give the students a robust hands-on experience in materials design

and a better opportunity to interact with CHiMaD researchers during their time in Chicago.

Adding to the diversity within CHiMaD community, Ricardo Komai has joined the CHiMaD technical staff as a materials design expert to help coordinate CHiMaD materials

design training efforts; the 1st CHiMaD Materials Design Workshop was held on September 24-15, 2015. Ricardo holds a Ph.D. from Northwestern University and is also currently

a Materials Design Engineer Postdoctoral Intern at QuesTek. Having been a part of the

URM programs during his education, he will also be working closely with CHiMaD associate director Begum Gulsoy, and CHiMaD director Juan De Pablo, to expand CHiMaD

outreach activities to under represented groups from institutions other than FSU. On this

note, a summer school has been planned at University of Chicago, in collaboration with

Chicago Collegiate Scholars Program for 2016. CHiMaD faculty from various institutions

will contribute to this effort by giving special lectures regarding their expertise.

On another effort, in 2015, QuesTek expanded its team of materials design engineers by

the addition of two female materials design experts, Dana Frankel and Ida Berglund, to the

QuesTek team. Prior to joining QuesTek, Dana Frankel was a CHiMaD-supported graduate

student at Northwestern University. Dana’s doctoral work focused on Shape Memory

Alloys, which she successfully defended in 2015. Minority student Shaleahk Wilson, a

QuesTek high-school intern in 2015, has been accepted to Northwestern University and

will begin in September 2016 as a Freshman majoring in Materials Science.
19.5 Other Outreach Activities Organized by CHiMaD PIs 153

19.5 Other Outreach Activities Organized by CHiMaD PIs

Workshop on Predictive Theoretical and Computational Approaches for Additive Manufacturing, Washington, DC, USA (10/7/15) Organizing PI: Jian Cao (NU)

Low-Dimensional Nanoelectronic Materials Use-Case Group Workshop, including a CHiMaD seminar by Davydov (NIST), Evanston, IL, USA (04/30/15) Organizing PI: Mark

Hersam (NU)

Directed Self-Assembly Professional Intensive Course, SPIE International Meeting, San

Jose, CA, USA (02/15) Organizing PI: Juan De Pablo (UC)

Additive Manufacturing, two junior undergraduate students participated in this project

where they learned basic principles of 3D printing and took part in a workshop "3D

Printing and Additive Manufacturing Science and Technology" co-hosted by the U.S.

Army Research Office and the University of North Carolina. One of the students wrote a

senior Research Thesis on this topic. Organizing PI: Alexander Umatsev (FSU)

Building the Materials Data Infrastructure: A Materials Community Planning Workshop

In-Process Materials Data for Modeling Workshop

Materials Data Analytics: A Path-Finding Workshop

Organizing PI: Larry Berardinis (ASM)

VI

20 Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

21 Information Dissemination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161

Information Dissemination

20. Publications

[1] Xiaolong Liu et al. “Rotationally Commensurate Growth of MoS2 on Epitaxial Graphene”.

In: ACS Nano 10.1 (2016), pages 1067–75. DOI: 10.1021/acsnano.5b06398 (cited on

pages 16, 19, 21).

[2] Deep Jariwala et al. “Large-Area, Low-Voltage, Antiambipolar Heterojunctions from SolutionProcessed Semiconductors”. In: Nano Letters 15.1 (2014), pages 416–421. DOI: 10.1021/

nl5037484 (cited on pages 19–21).

[3] Marc Z Miskin et al. “Turning statistical physics models into materials design engines”.

In: Proceedings of the National Academy of Sciences 113.1 (2016), pages 34–39. DOI:

10.1073/pnas.1509316112 (cited on pages 41, 43, 51, 63, 91).

[4] Leah K Roth and Heinrich M Jaeger. “Optimizing packing fraction in granular media

composed of overlapping spheres”. In: Soft matter 12.4 (2016), pages 1107–1115. DOI:

10.1039/C5SM02335A (cited on page 43).

[5] Sam Wilken, Marc Z Miskin, and Heinrich M Jaeger. “Optimizing a reconfigurable material

via evolutionary computation”. In: Physical Review E 92.2 (2015), page 022212. DOI:

10.1103/PhysRevE.92.022212 (cited on page 43).

[6] Tamar Segal-Peretz et al. “Characterizing the three-dimensional structure of block copolymers via sequential infiltration synthesis and scanning transmission electron tomography”.

In: ACS nano 9.5 (2015), pages 5333–5347. DOI: 10.1021/acsnano.5b01013 (cited on

pages 43, 51, 63, 91).

[7] Abelardo Ramirez-Hernandez et al. “A multichain polymer slip-spring model with fluctuating

number of entanglements for linear and nonlinear rheology”. In: The Journal of chemical

physics 143.24 (2015), page 243147. DOI: 10.1063/1.4936878 (cited on pages 43, 51, 63,

91).

[8] Brandon L Peters et al. “Graphoepitaxial assembly of cylinder forming block copolymers

in cylindrical holes”. In: Journal of Polymer Science Part B: Polymer Physics 53.6 (2015),

pages 430–441. DOI: 10.1002/polb.23652 (cited on pages 43, 51, 63, 91).

[9] Ha-Kyung Kwon et al. “Theoretical Analysis of Multiple Phase Coexistence in Polyelectrolyte Blends”. In: Macromolecules 48.16 (2015), pages 6008–6015. DOI: 10.1021/acs.

macromol.5b00901 (cited on page 51).
158 Chapter 20. Publications

[10] Zonghui Wei and Erik Luijten. “Systematic coarse-grained modeling of complexation between small interfering RNA and polycations”. In: The Journal of chemical physics 143.24

(2015), page 243146. DOI: 10.1063/1.4937384 (cited on pages 51, 53, 63).

[11] H Xu et al. “A Descriptor-Based Design Methodology for Developing Heterogeneous

Microstructural Materials System”. In: ASME. J. Mech. Des. 136.5 (2014), page 051007

(cited on page 63).

[12] Yichi Zhang et al. “Microstructure reconstruction and structural equation modeling for

computational design of nanodielectrics”. In: Integrating Materials and Manufacturing

Innovation 4.1 (2015), pages 1–26. DOI: 10 . 1186 / s40192 - 015 - 0043 - y (cited on

page 63).

[13] Zhao H. et al. “NanoMine: a material genome approach for polymer nanocomposites analysis

and design”. In: APL-Materials (2015, Accepted) (cited on page 63).

[14] Zhang M. et al. “Stiffness Gradients in Polymeric Model Nanocomposites Characterized

via Atomic Force Microscopy and Fluorescence Spectroscopy”. In: Macromolecules (2015,

Accepted) (cited on page 63).

[15] Irene Hassinger et al. “Toward the development of a quantitative tool for predicting dispersion

of nanocomposites under non-equilibrium processing conditions”. In: Journal of Materials

Science 51.9 (2016), pages 4238–4249. ISSN: 1573-4803. DOI: 10.1007/s10853- 015-

9698-1. URL: http://dx.doi.org/10.1007/s10853-015-9698-1 (cited on page 63).

[16] Chen Shao and Sinan Keten. “Stiffness Enhancement in Nacre-Inspired Nanocomposites

due to Nanoconfinement”. In: Scientific reports 5 (2015). DOI: 10.1038/srep16452 (cited

on page 63).

[17] Xin Qin et al. “Tuning Glass Transition in Polymer Nanocomposites with Functionalized Cellulose Nanocrystals through Nanoconfinement”. In: Nano letters 15.10 (2015), pages 6738–

6744. DOI: 10.1021/acs.nanolett.5b02588 (cited on page 63).

[18] D.I. Uzunov and A. Umantsev. “Equilibrium fluctuations in a metastable state of a Ginzburg–

Landau system”. In: Physica A: Statistical Mechanics and its Applications 448 (2016),

pages 283 –299. ISSN: 0378-4371. DOI: http : / / dx . doi . org / 10 . 1016 / j . physa .

2015 . 12 . 098. URL: http : / / www . sciencedirect . com / science / article / pii /

S0378437115011267 (cited on page 78).

[19] James E. Saal and C. Wolverton. “Energetics of antiphase boundaries in γ

0 Co3(Al,W)-

based superalloys”. In: Acta Materialia 103 (2016), pages 57 –62. ISSN: 1359-6454. DOI:

http : / / dx . doi . org / 10 . 1016 / j . actamat . 2015 . 10 . 007. URL: http : / / www .

sciencedirect.com/science/article/pii/S1359645415300070 (cited on page 78).

[20] W. K. Liu et al. “Linking process, structure, and property in additive manufacturing applications through advanced materials modellin”. In: COMPLAS XIII Conference Proceedings

(2015) (cited on page 78).

[21] Jacob Smith et al. “Linking process, structure, property, and performance for metal-based

additive manufacturing: computational approaches with experimental support”. In: Computational Mechanics (2016), pages 1–28. ISSN: 1432-0924. DOI: 10.1007/s00466-015-

1240-4. URL: http://dx.doi.org/10.1007/s00466-015-1240-4 (cited on pages 78,

96).

[22] Jacob Smith et al. “Thermodynamically consistent microstructure prediction of additively

manufactured materials”. In: Computational Mechanics (2016), pages 1–12. ISSN: 1432-

0924. DOI: 10.1007/s00466- 015- 1243- 1. URL: http://dx.doi.org/10.1007/

s00466-015-1243-1 (cited on pages 78, 96).
159

[23] Jacob Smith, Wing Kam Liu, and Jian Cao. “A general anisotropic yield criterion for

pressure-dependent materials”. In: International Journal of Plasticity 75 (2015). Special

Issue: Metal Forming – Challenges in Constitutive and Fracture Modeling, pages 2 –21.

ISSN: 0749-6419. DOI: http : / / dx . doi . org / 10 . 1016 / j . ijplas . 2015 . 08 . 009.

URL: http://www.sciencedirect.com/science/article/pii/S0749641915001461

(cited on pages 78, 96).

[24] Jacob Smith, Jian Cao, and Wing Kam Liu. “Computational Challenges and Future Research

Directions in Additive Manufacturing”. In: IACM Expression (2016 (In Press)) (cited on

pages 78, 96).

[25] Wentao Yan et al. “Multiscale modeling of electron beam and substrate interaction: a new heat

source model”. In: Computational Mechanics 56.2 (2015), pages 265–276. ISSN: 1432-0924.

DOI: 10.1007/s00466-015-1170-1. URL: http://dx.doi.org/10.1007/s00466-

015-1170-1 (cited on page 78).

[26] Wei Xiong and Gregory B. Olson. “Integrated computational materials design for highperformance alloys”. In: MRS Bulletin 40.12 (2015), pages 1035–1044 (cited on page 78).

[27] Wei Xiong and Gregory B Olson. “Cybermaterials: materials by design and accelerated

insertion of materials”. In: Npj Computational Materials 2 (Feb. 2016), 15009 EP –. URL:

http://dx.doi.org/10.1038/npjcompumats.2015.9 (cited on page 78).

[28] Jia-Yi Yan and G.B. Olson. “Computational thermodynamics and kinetics of displacive

transformations in titanium-based alloys”. In: Journal of Alloys and Compounds 673 (2016),

pages 441 –454. ISSN: 0925-8388. DOI: http://dx.doi.org/10.1016/j.jallcom.

2016 . 02 . 251. URL: http : / / www . sciencedirect . com / science / article / pii /

S0925838816305291 (cited on page 78).

[29] Dana J. Frankel and Gregory B. Olson. “Design of Heusler Precipitation Strengthened NiTiand PdTi-Base SMAs for Cyclic Performance”. In: Shape Memory and Superelasticity 1.2

(2015), pages 162–179. ISSN: 2199-3858. DOI: 10.1007/s40830- 015- 0017- 0. URL:

http://dx.doi.org/10.1007/s40830-015-0017-0 (cited on page 78).

[30] T. Jiang D. Frankel and G. B. Olson. “Design of Low-Ni and Ni-free PdTi-based SMAs”.

In: Materials Today: Proceedings. 2, Suuplement 3. Materials Today: Proc. ICOMAT 2014.

2015, S801–S804 (cited on page 78).

[31] Jing Zhou et al. “Direct atom probe tomography observations of concentration fluctuations

in Fe–Cr solid solution”. In: Scripta Materialia 98 (2015), pages 13 –15. ISSN: 1359-6462.

DOI: http : / / dx . doi . org / 10 . 1016 / j . scriptamat . 2014 . 10 . 035. URL: http :

//www.sciencedirect.com/science/article/pii/S1359646214004539 (cited on

page 78).

[32] S. Chan et al. “Multiscale ductile fracture integrating tomographic characterization and 3-D

simulation”. In: Acta Materialia 82 (2015), pages 503–510 (cited on page 78).

[33] N. J. Wengrenovich and G. B. Olson. “Design of a Structural TRIP Steel for Blast and

Fragment Protection”. In: Materials Today: Proceedings. 2, Supplement 3. Materials Today:

Proc. ICOMAT 2014. 2015 (cited on page 78).

[34] Alexander M Schneider et al. “Wide bandgap OPV polymers based on pyridinonedithiophene

unit with efficiency”. In: Chemical Science 6.8 (2015), pages 4860–4866. DOI: 10.1039/

C5SC01427A (cited on page 91).

[35] Luyao Lu et al. “High-performance ternary blend polymer solar cells involving both energy

transfer and hole relay processes”. In: Nat Commun 6 (2015), page 7327. DOI: 10.1038/

ncomms8327 (cited on page 91).
160 Chapter 20. Publications

[36] Luyao Lu et al. “Recent Advances in Bulk Heterojunction Polymer Solar Cells”. In: Chem

Rev 115.23 (2015), pages 12666–731. DOI: 10.1021/acs.chemrev.5b00098 (cited on

page 91).

[37] Luyao Lu et al. “Status and prospects for ternary organic photovoltaics”. In: Nature Photonics

9.8 (2015), pages 491–500. DOI: 10.1038/nphoton.2015.128 (cited on page 91).

[38] Nanjia Zhou et al. “All-Polymer Solar Cell Performance Optimized via Systematic Molecular

Weight Tuning of Both Donor and Acceptor Polymers”. In: Journal of the American Chemical

Society 138.4 (2016). PMID: 26719054, pages 1240–1251. DOI: 10.1021/jacs.5b10735.

eprint: http://dx.doi.org/10.1021/jacs.5b10735. URL: http://dx.doi.org/10.

1021/jacs.5b10735 (cited on pages 99, 101).

[39] B. Blaiszik et al. “Building a Materials Data Facility- Data Services to Advance Materials

Science Research”. In: Materials Science and Technology 2015 Conference Proceedings.

2015 (cited on page 113).

[40] K. Chard et al. “Globus Data Publication as a Service: Lowering Barriers to Reproducible

Science”. In: e-Science (e-Science), 2015 IEEE 11th International Conference on. 2015,

pages 401–410. DOI: 10.1109/eScience.2015.68 (cited on page 113).

[41] Michael J. Welland et al. “Miscibility Gap Closure, Interface Morphology, and Phase

Microstructure of 3D LixFePO4 Nanoparticles from Surface Wetting and Coherency Strain”.

In: ACS Nano 9.10 (2015). PMID: 26355590, pages 9757–9771. DOI: 10.1021/acsnano.

5b02555. eprint: http : / / dx . doi . org / 10 . 1021 / acsnano . 5b02555. URL: http :

//dx.doi.org/10.1021/acsnano.5b02555 (cited on page 117).

[42] John Mangeri et al. “Influence of Elastic and Surface Strains on the Optical Properties of

Semiconducting Core-Shell Nanoparticles”. In: Phys. Rev. Applied 4 (1 2015), page 014001.

DOI: 10.1103/PhysRevApplied.4.014001. URL: http://link.aps.org/doi/10.

1103/PhysRevApplied.4.014001 (cited on page 117).
21. Information Dissemination

21.1 Low-Dimensional Nanoelectronic Materials

1. X. Ren, L. Fang, A.K. Singh, F. Tavazza, A. Davydov, M. Kanatzidis, L. Lauhon,

Atom Probe Tomography Analysis of Doping in 2-D Materials, MRS Fall Meeting,

Boston, MA, USA (12/01/2015)

2. M. C. Hersam, Solution processing and device integration of two-dimensional black

phosphorus, 228th Electrochemical Society Meeting, Phoenix, AZ, USA (10/14/15).

3. M. C. Hersam, Fundamentals and applications of two-dimensional nanomaterial

heterostructures, National Academies Symposium on Novel Materials, Washington,

DC, USA (10/7/15).

4. M. C. Hersam, Chemically modified two-dimensional nanoelectronic heterostructures, China Nano 2015, Beijing, China (9/3/15).

5. M. C. Hersam, Integration challenges and opportunities for two-dimensional materials, Science and Technology of Two-Dimensional Materials Workshop, Orlando,

FL, USA (8/21/15).

6. M. C. Hersam, Solution processing and device integration at the two-dimensional

limit, 250th American Chemical Society National Meeting, Boston, MA, USA

(8/17/15).

7. M. C. Hersam, Carbon and related nanomaterial heterostructure devices, Carbonhagen Symposium on Carbon and Related Nanomaterials, Copenhagen, Denmark

(8/13/15).

8. M. C. Hersam, Nanomaterial heterostructures for electronic and electrochemical

applications, 8th International Conference on Materials for Advanced Technologies,

Singapore (7/1/15).

9. M. C. Hersam, Beyond carbon nanotube thin-film transistors: Logic circuits, memory, and heterostructures, 16th International Conference on the Science and Application of Nanotubes, Nagoya, Japan (6/30/15).

10. M. C. Hersam, Solution processing and device integration of two-dimensional black

phosphorus, 6th Graphene and 2D Materials Satellite Symposium, Nagoya, Japan

(6/28/15).

11. M. C. Hersam, Emerging device applications for two-dimensional nanomaterial

heterostructures, 73rd Device Research Conference, Columbus, OH, USA (6/23/15).
162 Chapter 21. Information Dissemination

12. M. C. Hersam, Production and application of printable carbon nanomaterial

inks, 2015 TechConnect World Innovation Conference, National Harbor, MD, USA

(6/17/15).

13. M. C. Hersam, Anti-ambipolar, gate-tunable p-n heterojunctions, 227th Electrochemical Society Meeting, Chicago, IL, USA (5/25/15).

14. M. C. Hersam, VLSI carbon nanotube thin-film circuits, 227th Electrochemical

Society Meeting, Chicago, IL, USA (5/25/15).

15. M. C. Hersam, Ubiquitous electronics and sensors: Enabling the internet of things,

Illinois Chemical Education Foundation Scholarship Luncheon, Des Plaines, IL,

USA(5/21/15).

16. M. C. Hersam, Solution processing and device integration at the two-dimensional

limit, European Materials Research Society Spring Meeting, Lille, France (5/13/15).

17. M. C. Hersam, Integration challenges and opportunities for two-dimensional materials, NSF US-EU Workshop on 2D Layered Materials and Devices, Arlington, VA,

USA (4/23/15).

18. M. C. Hersam, Integrating carbon nanomaterials into functional heterostructure

devices, Materials Research Society Spring Meeting, San Francisco, CA, USA

(4/10/15).

19. M. C. Hersam, Chemically modified two-dimensional nanoelectronic heterostructures, 249th American Chemical Society National Meeting, Denver, CO, USA

(3/23/15).

20. M. C. Hersam, Carbon nanomaterial heterostructure devices, 49th American Chemical Society National Meeting, Denver, CO, USA (3/22/15).

21. M. C. Hersam, Nanomaterial heterostructures for electronic and energy technologies, Workshop on Semiconductors, Electronic Materials, Thin Film, and Photonic

Materials, Tel Aviv, Israel (2/23/15).

22. M. C. Hersam, Integrating monodisperse nanomaterials into heterostructure devices,

Towards Reality in Nanoscale Materials VIII Workshop, Levi, Finland (2/9/15).

23. M. C. Hersam, Nanomaterial heterostructures for electronic and energy technologies, Muju International Winter School, Muju Deogyusan Resort, Korea (1/29/15).

24. M. C. Hersam, Integrating nanomaterials into heterostructure devices and systems,

National Research Council of Canada Colloquium, Ottawa, Canada (1/23/15).

25. M. C. Hersam, Integrating nanomaterials into heterostructure devices and systems, Northwestern University Department of Physics and Astronomy Colloquium,

Evanston, IL, USA(1/9/15).

21.2 Data Mining

1. L. Ward, A. Agrawal, A. Choudhary, C. Wolverton, General Machine-LearningBased Framework for Predicting the Properties of Materials Using Composition.

Integrating Experiments, Simulations and Machine Learning to Accelerate Materials

Innovation, MRS Fall 2015. Boston, MA, USA (12/02/15)

2. A. Choudhary, Transforming Materials Design to Marketing with BIG DATA SCIENCE + SUPERCOMPUTING?, 2015 Whitney Symposium GE Global Research,

Niskayuna, NY, USA (10/12/15)

3. A. Agrawal, Towards an Infrastructure for Materials Data Analytics: An Overview

of Tools and Platforms for Code Development, Collaboration, and Data Analytics, Materials Research Collaboration Environment Workshop, Dayton OH, USA
21.3 In-Situ Si Composites 163

(08/13/15)

4. L. Ward, A. Agrawal, A. Choudhary, C. Wolverton, Accelerating Materials Discovery with Machine Learning. Computational Materials Chemistry, Telluride Science

and Research Center, Telluride, CO, USA. (06/27/15)

5. W. Chen, Design of Engineered Nano-materials Systems, School of Civil and Environmental Engineering Seminar, The University of New South Wales, Sydney,

Australia (06/11/15)

6. W. Chen, Design of Complex System for the 21st Century Industrial Engineering

Seminar, University of Oklahoma, Norman, OK, USA (04/03/15)

7. W. Chen, Design of Emerging Materials Systems, Mechanical Engineering Department Seminar, Texas A&M University, College Station, TX, USA (03/25/15)

8. A. Agrawal, CHiMaD Data Mining: Fatigue, 30th Steel Research Group Meeting

Northwestern University, Evanston, IL USA (03/23/15)

9. L. Ward, V. Hedge, A. Agrawal, A. Choudhary, C. Wolverton, Accelerating Materials Discovery with Machine Learning. Computational Modeling and Stochastic

Methods for Materials Discovery and Properties, TMS 2015. (03/17/15)

10. L. Ward, A. Agrawal, A. Choudhary, C. Wolverton, Machine Learning for Materials

Discovery. (Poster Presentation). Machine Learning for Many Particle Systems,

Institute for Pure and Applied Mathematics, UCLA, CA, USA (02/23/15)

11. L. Ward, V. Hedge, A. Agrawal, A. Choudhary, C. Wolverton, High-Throughput

Computation and Machine Learning for Materials Discovery (Poster Presentation).

Opportunities for Materials Informatics 2015. University of Wiconsin-Madison,

Madison, WI, USA (02/09/15)

21.3 In-Situ Si Composites

1. P. W. Voorhees The Center for Hierarchical Materials Design: Realizing the Vision

of the Materials Genome Initiative. International Summit Forum on the Materials

Genome Initiative, China-MGI-2015, Shanghai China (08/15).

2. P. W. Voorhees The Center for Hierarchical Materials Design: Realizing the Vision

of the Materials Genome Initiative HERO-M meeting, Stockholm, Sweden (05/15).

3. P. W. Voorhees Eutectic Solidification: from Multicomponent alloys to the Macroscale

TMS Annual Meeting, Orlando FL, USA (03/15).

21.4 Soft Matter Design Based on Charge Complexation

1. M. Olvera de la Cruz All-Polymer Solar Cell Performance Optimized via Systematic

Molecular Weight Tuning of both Donor and Acceptor Polymers Advances and

Challenges in Soft Photovoltaic Research, University of Chicago, Chicago, IL, USA

(11/13/15)

2. E. Luijten Colloidal Structures from Unexpected Interactions: From Dielectric

Many-Body Effects to Non- Equilibrium Phase Behavior Chemical Physics Seminar,

California Institute of Technology, CA, USA (11/15)

3. E. Luijten Exploiting multivalent interactions to create new drug and gene delivery

systems International Symposium: Multivalent Interactions in Polyelectrolytes: New

Physics, Biology and Materials, Institute for Molecular Engineering, University of

Chicago, Chicago, IL, USA (10/15)
164 Chapter 21. Information Dissemination

4. M. Olvera de la Cruz Ion clusters in neutral-charged polymer blends and copolymers ACS National Meeting, Boston, MA, USA (09/16/15)

5. M. Olvera de la Cruz Ion clusters in neutral-charged polymer blends and copolymers Beijing Institute of Technology, Beijing, China (09/05/15)

6. E. Luijten The future of biomolecular modeling: Is there still room for coarsegrained models Midwest Computational Biomolecular Modeling Symposium, Beckman Institute for Advanced Science and Technology, University of Illinois, Urbana,

IL, USA (09/15)

7. J. de Pablo American Institute of Chemical Engineering National Meeting (09/15)

8. J. de Pablo Frauenhofer Institute International Meeting on Computational Design of

Lithographic Processes, Germany (09/15)

9. E. Luijten Conformation and Diffusion of DNA-Coated Nanoparticles Symposium:

Colloid Polymer Achitectures and Mixtures, Fall Meeting of the American Chemical

Society, Boston, MA, USA (08/15)

10. E. Luijten Self-Assembly in Colloidal Systems: New Insights through Novel Algorithms Colloquium, Department of Mathematics, Pennsylvania State University,

State College, PA, USA (08/15)

11. E. Luijten Self-Assembled Gene Carriers of DNA and Graft Copolymers Symposium:

Theory and Modeling of Nanoparticle Interactions with Biomolecules and Polymers,

Fall Meeting of the American Chemical Society, Boston, MA, USA (08/15)

12. M. Olvera de la Cruz Ion clusters in neutral-charged polymer blends and copolymers 1

st US-Japan Materials Genome Workshop, Japan (06/22/15)

13. E. Luijten Dynamic Collective Behavior and Phase Separation of Active Colloids

Complex matter physics: Active materials, dynamics and patterns, Havana, Cuba

(06/15)

14. J. de Pablo International Symposium on Self Assembly, Taipei, Taiwan (06/15)

15. E. Luijten Colloidal Structures from Unexpected Interactions: From Dielectric

Many-Body Effects to Non- Equilibrium Phase Behavior Theoretical Chemistry

Seminar, Department of Chemistry, Cambridge University, U.K (05/15)

16. E. Luijten Geometric Cluster Algorithm for Anisotropic Particles Theory & Simulation Seminar, Department of Physics and Astronomy, Utrecht University, The

Netherlands, (05/15)

17. M. Olvera de la Cruz Ion clusters in neutral-charged polymer blends and copolymers Penn State University, State College, PA, USA (04/21/15)

18. E. Luijten New Insights into Some Principles of Self-Assembly Keynote address:

Theory and Principles of Self-Assembly, 12th Annual Conference on the Foundations

of Nanoscience (FNANO15), Snowmass, Utah, USA (04/15)

19. J. de Pablo Materials Research Society National Meeting, (04/15)

20. J. Zwanikken, M. Olvera de la Cruz Tuning the phase diagram of polyelectrolyte

blends with a pinch of salt APS Spring Meeting, San Antonio, TX, USA (03/3/15)

21. H-K. Kwon, M. Olvera de la Cruz Formation of ion clusters in the phase separated

structures of neutral-charged polymer blends APS Spring Meeting, San Antonio,

TX, USA (03/3/15)

22. E. Luijten Patchy Colloids: Collective behavior of Janus particles Three-hour

lecture series at Geilo School on Cooperative particles: Patchy colloids, active matter

and nanofluids, Institute for Energy Technology, Geilo, Norway (03/15)

23. J. de Pablo American Chemical Society National Meeting, San Diego, CA, USA

(03/15)
21.5 Directed Self-Assembly of Block Copolymers for Lithography 165

24. J. de Pablo American Physical Society National Meeting, Baltimore, MD, USA

(03/15)

25. E. Luijten Colloidal Self-Assembly Controlled by Dielectric Many-Body Effects

Nano Seminar,Debye Institute, Utrecht University, The Netherlands, (02/15)

26. E. Luijten Accelerated Monte Carlo Methods for Computational Materials Science

CIERA (Center for Interdisciplinary Exploration and Research in Astrophysics),

Northwestern University, (02/15)

27. J. de Pablo SPIE International Lithography Meeting, San Jose, CA, USA (02/15)

28. J. de Pablo Photopolymer International Conference, Chiba, Japan, 2015

21.5 Directed Self-Assembly of Block Copolymers for Lithography

1. H. Jaeger IME Industrial Affiliates Meeting, University of Chicago, Chicago, IL,

USA (11/12/15)

2. H. Jaeger plenary lecture at Particles 2015, Barcelona, Spain (10/29/15)

3. J. de Pablo American Institute of Chemical Engineering National Meeting (09/15)

4. J. de Pablo Frauenhofer Institute International Meeting on Computational Design of

Lithographic Processes, Germany (09/15)

5. H. Jaeger Designer Matter Workshop, AMOLF, Amsterdam, The Netherlands

(06/33/15)

6. J. de Pablo International Symposium on Self Assembly, Taipei, Taiwan (06/15)

7. J. Raybin, S. Sibener Chemical and Biological Defense Science & Technology

Conference, St. Louis, MO, USA (05/12/15)

8. H. Jaeger Active Matter Summit, MIT, Boston, MA, USA (04/25/15)

9. J. de Pablo Materials Research Society National Meeting, (04/15)

10. J. de Pablo American Chemical Society National Meeting, San Diego, CA, USA

(03/15)

11. H. Jaeger APS March Meeting, San Antonio, TX, USA (03/03/15)

12. J. de Pablo American Physical Society National Meeting, Baltimore, MD, USA

(03/15)

13. J. de Pablo Directed self-assembly at molecular length scales and the interplay

between experiment, theory, and simulation SPIE International Lithography Meeting,

San Jose, CA, USA (02/24/15)

14. H. Jaeger Plenary Lecture, Annual meeting of Dutch Physical Society, Veldhoven,

The Netherlands (01/20/15)

15. J. de Pablo Photopolymer International Conference, Chiba, Japan, 2015

21.6 Polymer Matrix Materials

1. E. Luijten Colloidal Structures from Unexpected Interactions: From Dielectric

Many-Body Effects to Non- Equilibrium Phase Behavior Chemical Physics Seminar,

California Institute of Technology, CA, USA (11/15)

2. S. Keten, Invited Talk, MRS Fall Meeting, Boston, MA, USA (11/15)

3. E. Luijten Exploiting multivalent interactions to create new drug and gene delivery

systems International Symposium: Multivalent Interactions in Polyelectrolytes: New

Physics, Biology and Materials, Institute for Molecular Engineering, University of

Chicago, Chicago, IL, USA (10/15)
166 Chapter 21. Information Dissemination

4. E. Luijten The future of biomolecular modeling: Is there still room for coarsegrained models Midwest Computational Biomolecular Modeling Symposium, Beckman Institute for Advanced Science and Technology, University of Illinois, Urbana,

IL, USA (09/15)

5. J. de Pablo American Institute of Chemical Engineering National Meeting (09/15)

6. J. de Pablo Frauenhofer Institute International Meeting on Computational Design of

Lithographic Processes, Germany (09/15)

7. E. Luijten Conformation and Diffusion of DNA-Coated Nanoparticles Symposium:

Colloid Polymer Achitectures and Mixtures, Fall Meeting of the American Chemical

Society, Boston, MA, USA (08/15)

8. E. Luijten Self-Assembly in Colloidal Systems: New Insights through Novel Algorithms Colloquium, Department of Mathematics, Pennsylvania State University,

State College, PA, USA (08/15)

9. E. Luijten Self-Assembled Gene Carriers of DNA and Graft Copolymers Symposium:

Theory and Modeling of Nanoparticle Interactions with Biomolecules and Polymers,

Fall Meeting of the American Chemical Society, Boston, MA, USA (08/15)

10. S. Keten, Mesoscale Modeling of the Interfacial Mechanics of Nanocellulose Composites, 2015 TAPPI International Conference on Nanotechnology for Renewable

Materials, Atlanta, GA, USA (06/24/15)

11. S. Keten, Discerning Nanoconfinement Effects in Bioinspired Nanocomposites

Through Predictive Multi-scale Simulations, ASCE Engineering Mechanics Institute

Conference, Standford, CA, USA (06/17/15)

12. W. Chen, Design of Engineered Nano-materials Systems, School of Civil and Environmental Engineering Seminar, The University of New South Wales, Sydney,

Australia (06/11/15)

13. E. Luijten Dynamic Collective Behavior and Phase Separation of Active Colloids

Complex matter physics: Active materials, dynamics and patterns, Havana, Cuba,

(06/15)

14. J. de Pablo International Symposium on Self Assembly, Taipei, Taiwan (06/15)

15. E. Luijten Colloidal Structures from Unexpected Interactions: From Dielectric

Many-Body Effects to Non- Equilibrium Phase Behavior Theoretical Chemistry

Seminar, Department of Chemistry, Cambridge University, U.K (05/15)

16. E. Luijten Geometric Cluster Algorithm for Anisotropic Particles Theory & Simulation Seminar, Department of Physics and Astronomy, Utrecht University, The

Netherlands, (05/15)

17. W. Chen, Design of Complex System for the 21st Century Industrial Engineering

Seminar, University of Oklahoma, Norman, OK, USA (04/03/15)

18. E. Luijten New Insights into Some Principles of Self-Assembly Keynote address:

Theory and Principles of Self-Assembly, 12th Annual Conference on the Foundations

of Nanoscience (FNANO15), Snowmass, Utah, USA (04/15)

19. J. de Pablo Materials Research Society National Meeting (04/15)

20. W. Chen, Design of Emerging Materials Systems, Mechanical Engineering Department Seminar, Texas A&M University, College Station, TX, USA (03/25/15)

21. S. Keten, Stick-slip Shear Phenomenon of Interfaces Between Cellulose Nanocrystals, American Chemical Society National Meeting & Exposition, Denver, CO, USA

(03/22/15)

22. E. Luijten Patchy Colloids: Collective behavior of Janus particles Three-hour

lecture series at Geilo School on Cooperative particles: Patchy colloids, active matter
21.7 Precipitation-Strengthened Alloys 167

and nanofluids, Institute for Energy Technology, Geilo, Norway (03/15)

23. E. Luijten Colloidal Self-Assembly Controlled by Dielectric Many-Body Effects

Nano Seminar,Debye Institute, Utrecht University, The Netherlands (02/15)

24. E. Luijten Accelerated Monte Carlo Methods for Computational Materials Science

CIERA (Center for Interdisciplinary Exploration and Research in Astrophysics),

Northwestern University, Evanston, IL, USA (02/15)

25. J. de Pablo American Chemical Society National Meeting, San Diego, CA, USA

(03/15)

26. J. de Pablo American Physical Society National Meeting, Baltimore, MD, USA

(03/15)

27. S. Keten, Department Seminar, MIT, Boston, MA, USA (2015)

28. S. Keten, Department Seminar, Brown University, Providence, RI, USA (2015)

29. S. Keten, Department Seminar, Columbia University, New York, NY, USA (2015)

30. S. Keten, Seminar, SES Conference, (2015)

31. S. Keten, Seminar, Dow Chemical Company (2015)

32. J. de Pablo Photopolymer International Conference, Chiba, Japan, 2015

21.7 Precipitation-Strengthened Alloys

1. G. Olson Atoms to Metals DOE-NETL ICME Workshop, Chicago IL, USA (11/30/15)

2. G. Olson Materials by Design: Accelerating Qualification Lockheed-Martin/DARPA

Workshop on Generative Design, Arlington VA, USA (11/12/15)

3. G. Olson Osmond & Martens: Hardenite Past & Future Plenary Lecture, ASM Heat

Treating Society Annual Meeting, Detroit MI, USA (10/20/15)

4. G. Olson Microstructural Optimization in High Hardness Steels Audi Tech Center,

Inglestadt, Germany (10/9/15)

5. G. Olson Technology Acceleration through Phase Discovery Constellium Technology Center, Voreppe, France, (10/7/15)

6. G. Olson Data-Driven Discovery for Designed Thermoelectrics DARPA SIMPLEX

Program Review Meeting, American Museum of Natural History, NY, NY, USA

(9/29/15)

7. D. Sauza, P. Boccini, D. Dunand, D. Seidman Microstructure and Mechanical

Properties of novel γ

0

-strengthened Co-based Superalloys Alcoa Technical Center,

Pittsburgh, PA, USA (07/30/15)

8. G. Olson Materials Genomics: From CALPHAD to Flight Illinois Science & Technology Council (ISTC) Congressional Briefing on Illinois Materials Technology,

Washington, DC, USA (7/21/15)

9. G. Olson Materials Genomics Benet Laboratory, Watervliet, NY, USA (7/9/15)

10. G. Olson Materials Genomics: From CALPHAD to Flight Knowles Atomic Power

Lab, Niskayuna, NY, USA (7/8/15)

11. G. Olson Flying Martensite: Transformations in Materials Design PTM2015, International Conference on Solid-Solid Transformations in Inorganic Materials, Vancouver, BC, Canada (07/2/15)

12. A. Umanstev Formation of Amorphous Nanophases, International Conference on

Solid-Solid Phase Transformations in Inorganic Materials (PTM 2015), Whistler,

CANADA (06/28/15)

13. G. Olson Materials Genomics: From CALPHAD to Flight Plenary Lecture, US/Japan

Materials Genome Workshop, Tsukuba, Japan, (6/24/15)
168 Chapter 21. Information Dissemination

14. A. Umantsev Nucleation at Large Driving Forces, Workshop for Theorists at Undergraduate Institutions, Kavli Institute for Theoretical Physics, UCSB, Santa Barbara

CA, USA (06/22/15)

15. W. Chen Design of Engineered Nano-materials Systems, Seminar, School of Civil

and Environmental Engineering, The University of New South Wales, Newport, UK

(06/11/15)

16. D. Seidman, P. Bocchini, D. Sauza, D. Dunand, Co-based superalloys with γ(f.c.c.)

plus γ

0

(L12) microstructure, NIST, Gaithersburg, MD, USA (06/05/2015)

17. G. Olson Materials Genomics: From CALPHAD to Flight Plenary Lecture, 3rd

ICME World Congress, Colorado Springs, CO, (06/01/15)

18. G. Olson Genomic Materials Design: Advanced Alloys CHiMaD Materials Design

Center 1st Annual Review Meeting, Evanston, IL, USA (05/01/15)

19. G. Olson Data-Driven Discovery for Designed Thermoelectrics DARPA SIMPLEX

Program Kickoff Meeting, Arlington, VA, USA (4/20/15)

20. G. Olson Materials Genome Initiative EAB Meeting, Michigan Technological University, Houghton, MI, USA(4/15/15)

21. G. Olson Integrated Computational Materials Design: From Genome to Flight

NU-Shanghai Jiao-Tong University Materials Genome Workshop, Evanston, IL

(4/13/15)

22. D. Dunand Novel Cobalt-based Superalloys, Briefing to Shanghai Jiao Tong University delegation, Northwestern University, Evanston, IL, USA (04/07/15)

23. D. Dunand Novel Cobalt-based Superalloys, Department Seminar, Mechanical

Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA, USA

(04/07/15)

24. W. Chen Design of Complex System for the 21st Century, Invited Seminar, Industrial

Engineering, University of Oklahoma, Norman, OK, USA (04/03/15)

25. G. Olson Data & Knowledge Fusion in Materials Informatics 3rd NU-NIMS Materials Genome Workshop, Evanston IL, USA (3/25/15)

26. W. Chen Design of Emerging Materials Systems, Department Seminar, Mechanical

Engineering, Texas A&M University, College Station, TX, USA (03/25/15)

27. G. Olson Aerospace Alloys by Design: From CALPHAD to Flight TMS2015, Orlando, FL, USA (3/17/15)

28. G. Olson Integrated Computational Materials Design: From Genome to Flight

TMS2015, Orlando, FL, USA (3/16/15)

29. G. Olson Genomic Materials Design: From Metals to Polymers University of

Chicago, Chicago, IL, USA (2/17/15)

30. G. Olson Genomic Materials Design: From CALPHAD to Flight SpaceX Technology

Center, Los Angeles, CA, USA (1/29/15)

31. G. Olson Advances in Materials Genomics: Making CyberSteels Fly APS Colloquium, Argonne National Laboratory, Lemont, IL, USA (1/7/15)

32. A. Umantsev Long-Period Stacking Fault Structures of Magnesium-Base Alloys,

Keynote, International Conference PLASTICITY 2015, Montego Bay, Jamaica

(01/06/15)

21.8 Organic Bulk Heterojunction Polymer Solar Cells

1. M. Goldey, G. Galli Electronic Structure Properties as Signatures of Morphological

Motifs in Organic Photovoltaics Advances and Challenges in Soft Matter Photovoltaic
21.9 Materials Data Facility 169

Research, University of Chicago, Chicago, IL, USA (11/13/15)

2. M. Goldey, G. Galli Organic Solar Cell Models Predict How Structures Change

Properties Mindbytes Research Computing Expo, University of Chicago, Chicago,

IL, USA (10/27/15)

3. L.Yu, Department Seminar, Macromolecules Science and Engineering Institute,

University of Michigan, Ann Arbor, MI, USA (10/12/15)

4. M. Goldey, G. Galli First Principles Simulations of Organic Solar Cells Argonne

Postdoctoral Research and Career Symposium, Argonne National Laboratory, Lemont,

IL, USA (10/08/15)

5. M. Goldey, A. Scheneider Organic Bulk Heterojunction Polymer Solar Cells Materials Design CHiMaD Materials Design Workshop, CHiMaD Headquarters, Evanston,

IL, USA (09/25/15)

6. J. de Pablo American Institute of Chemical Engineering National Meeting (09/15)

7. J. de Pablo Frauenhofer Institute International Meeting on Computational Design of

Lithographic Processes, Germany (09/15)

8. L.Yu, Plenary Talk, Organic solid state electronics, Beijing, China (08/08/15)

9. J. de Pablo International Symposium on Self Assembly, Taipei, Taiwan (06/15)

10. J. de Pablo Materials Research Society National Meeting (04/15)

11. J. de Pablo American Chemical Society National Meeting, San Diego, CA, USA

(03/15)

12. J. de Pablo American Physical Society National Meeting, Baltimore, MD, USA

(03/15)

13. J. de Pablo Photopolymer International Conference, Chiba, Japan, 2015

21.9 Materials Data Facility

1. B. Blaiszik, K. Chard, I. Foster. The Materials Data Facility (MDF): Data Services

to Advance Materials Research, Integrated Imaging Initiative Seminar, Argonne

National Laboratory, Lemont, IL, USA (12/15)

2. B. Blaiszik, K. Chard, I. Foster. The Materials Data Facility (MDF): Data Services

to Advance Materials Research, National Center for Supercomputing Applications

Joint Materials Science Seminar, University of Illinois at Urbana-Champaign, Urbana,

IL, USA (12/15)

3. I. Foster, B. Blaiszik, K. Chard. The Materials Data Facility, 4th National Data

Service Consortium Workshop, San Diego, CA, USA (10/15)

4. B. Blaiszik, K. Chard, J. Pruyne, J. Towns, S. Tuecke, I. Foster. The Materials Data

Facility (MDF): Data Services to Advance Materials Research, Materials Science

and Technology Conference, Columbus, OH, USA (10/15)

5. B. Blaiszik, K. Chard, R. Ananthakrishnan, J. Pruyne, J. Wozniak, M. Wilde, R.

Osborn, S. Tuecke, I. Foster. Globus Research Data Management Services and

the Materials Data Facility, Center for PRedictive Integrated Structural Materials

Science (PRISMS) Annual Meeting, Ann Harbor, MI, USA (09/15)

21.10 Additive Manufacturing

1. J. Cao Additive Manufacturing Predictive Theoretical and Computational Approaches

for Additive Manufacturing Workshop by U.S. National Committee on Theoretical

and Applied Mechanics (USNC/TAM), Washington, D.C, USA (10/7/15)
170 Chapter 21. Information Dissemination

2. J. Bennett Introduction to Predictive Theoretical and Computational Approaches to

Additive Manufacturing Instructor, Predictive Theoretical and Computational Approaches to Additive Manufacturing Workshop, 13th Annual US National Congress

on Computational Mechanics, San Diego, CA (07/26/15)

21.11 Coarse Grained Simulations of Organic Polymer Solar Cells

1. M. Olvera de la Cruz All-Polymer Solar Cell Performance Optimized via Systematic

Molecular Weight Tuning of both Donor and Acceptor Polymers Advances and

Challenges in Soft Photovoltaic Research, University of Chicago, Chicago, IL, USA

(11/13/15)

2. M. Olvera de la Cruz Ion clusters in neutral-charged polymer blends and copolymers ACS National Meeting, Boston, MA, USA (09/16/15)

3. M. Olvera de la Cruz Ion clusters in neutral-charged polymer blends and copolymers Beijing Institute of Technology, Beijing, China (09/05/15)

4. M. Olvera de la Cruz Ion clusters in neutral-charged polymer blends and copolymers 1

st US-Japan Materials Genome Workshop, Japan (06/22/15)

5. M. Olvera de la Cruz Ion clusters in neutral-charged polymer blends and copolymers Penn State University, State College, PA, USA (04/21/15)

21.12 Phase Field Methods

1. D. Chopp The Locally Adaptive Time Stepping (LATS) Method: From HodgkinHuxley to Cahn-Hilliard Applied Math Colloquium, University of Washington,

Seattle, WA (04/29/15)

2. O. Heinonen Length Scale Bridges Seminar, Los Alamos National Laboratory, Los

Alamos, NM, USA (04/23/15)

3. O. Heinonen Coupling Molecular Dynamics and First-principle Electronic Structure

Modeling of Disordered Heterostructures Invited presentation, "High Performance

and Parallel Computing for Materials Defects and Multiphase Flows” Workshop,

National University of Singapore, Singapore (01/15/15)

21.13 ASM Educational Outreach

1. L. Berardinis Materials Data Infrastructure Development Efforts: Update and Forecast Materials Science & Technology Conference (MS&T 2015), Columbus , OH,

USA (10/04/15)

2. L. Berardinis Innovation Showcase Digital Manufacturing and Design Innovation

Institute Project Call Workshop (DMDII-15-11 Completing the Model-Based Definition), Chicago, IL, USA (09/10/15)

3. L. Berardinis CMD Network: Working for the Materials Data Community Materials

Research Collaboration Environment Workshop, Dayton, OH, USA (08/13/15)

4. L. Berardinis A Review of Materials Data Infrastructure Projects 3rd World Congress

on Integrated Computational Materials Engineering (ICME 2015), Colorado Springs,

CO, USA (05/31/15)

5. L. Berardinis ASM announces winners of Materials Genome Toolkit Competition

CMD Network e-newsletter Articles (03/14/15)
21.13 ASM Educational Outreach 171

6. L. Berardinis CHiMaD Consortium Releases First Annual Report CMD Network

e-newsletter Articles (03/14/15)

7. L. Berardinis Materials Genome Initiative Poster Presentation, 6th North American

Materials Education Symposium, Columbus , OH, USA (03/26/15)

8. L. Berardinis Judging set to begin on ASM Materials Genome Toolkit Competition

CMD Network e-newsletter Articles (03/12/15)

9. L. Berardinis Inventory of Materials Data Infrastructure Development Projects

and Related Initiatives Building the Materials Data Infrastructure: A Materials

Community Planning Workshop, Arlington, VA, USA (01/21/15)
CH MaD