NISTIR 8038
 

Materials Genome Initiative: 

Materials Data 

Charles H. Ward 

James A. Warren 

This publication is available free of charge from: 

http://dx.doi.org/10.6028/NIST.IR.8038 
 

 

 

 

 

 

 

 

 

NISTIR 8038 

Materials Genome Initiative: 

Materials Data 

Charles H. Ward 

Materials and Manufacturing Directorate 

Air Force Research Laboratory 

James A. Warren 

Material Measurement Laboratory 

National Institute of Standards and Technology 

This publication is available free of charge from: 

http://dx.doi.org/10.6028/NIST.IR.8038 

January, 2015 

U.S. Department of Commerce 

Penny Pritzker, Secretary 

National Institute of Standards and Technology 

Willie May, Acting Under Secretary of Commerce for Standards and Technology and Acting Director 
Workshop Summary:

Materials Genome Initiative: Materials Data

15 - 16 July 2014

Dayton, Ohio

ii
DISCLAIMER: Certain commercial equipment, instruments, software, or materials are identified

in this paper in order to specify the procedures of experiments and simulations adequately.

Such identification is not	intended to imply recommendation or endorsement	by the National

Institute of Standards and Technology, nor is it	intended to imply that	the software, materials,	

instruments, or equipment	identified are necessarily the best	available for the purpose.

iii
Introduction

key objective for the Materials Genome Initiative (MGI) is access to digital	materials data.

Access may be for an	individual, a team, company, or an entire community	of interest. There are

several challenges	to providing access	to digital materials	data for which there are presently no

universally	accepted best practices. Under the MGI, Federal agencies have funded research and

development designed	to	explore, develop, and apply MGI methodologies. Several of these	efforts

either focus on an infrastructure	for sharing	data, or emphasize	data sharing as a key	requirement of the

overall technical effort.

One objective of starting several MGI-focused programs concurrently was that	the approaches,

lessons learned, and best practices would be	shared between programs in order to accelerate	the

overall development of a materials innovation	infrastructure (MII). This workshop	assembled

representatives from Federally funded efforts where a critical component	of	the overall research effort	

involves sharing of materials data in digital	format.	 The intent of this workshop was for	the participants

to provide insights into the goals of	their	effort, details of	their	approach to sharing digital materials

data, challenges they face, and	lessons learned. This exchange	of ideas will not only enhance	the

likelihood for success of the individual	efforts, but will	also speed the community’s understanding of

how best to	proceed	in	developing an	MII.

During the workshop, participants were asked to identify the top three next steps needed to be

taken within the materials community	and by	government agencies in addressing the challenges

associated with digital materials data. The following is list of priorities,	derived from those inputs, for	

each sector

Community

• Develop and deploy standards for data and	federated/collaborative environments

• Communicate value and	need	for digital materials data

• Define critical data to be compiled

• Engage Community

• Explore and leverage data	solutions developed outside materials community

• Train students in these emerging areas

• Establish community norms for publishing materials data

Government

• Lead data strategy/approach development

• Incentivize data deposition

• Facilitate	data	deposition (policy and technology)

• Support data	sharing and deposition (provide resources)

• Support data	creation

• Enhance data	management competency

• Provide	quality datasets for model development/validation

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2
 
MGI Materials Data Workshop Organizing Committee

Air Force Research Laboratory (AFRL)

Dr. Charles	Ward & Mr. Clare Paul

Army Research Laboratory (ARL)

Dr. John Beatty & Mr. Wayne Ziegler

Energy Efficiency and Renewable Energy, Department of	Energy (DOE-EERE)

Mr. William	Joost

National Aeronautics and Space Administration (NASA)

Dr. Stephen Smith & Dr. Dennis	Griffin

National Institute of Standards	and Technology (NIST)

Dr. James Warren

Office of Naval Research (ONR)

Dr. William	Mullins & Dr. Kenny Lipkowitz

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Agenda

MGI Materials Data Workshop

Tec^Edge, 5000 Springfield Street, Dayton, OH

1 - 16 July 2014

Tuesday

074 – 0800 Check-in

080 – 0815 Chuck Ward	(AFRL) – Introductory remarks

Discussion Lead: Will Joost (DOE-EERE)

081 – 0845 Jim Warren (NIST)	– Data Infrastructure

084 – 0915 John Allison / Brian	Puchala (Univ. Michigan)	– PRISMS/ALMMII

091 – 0945 Carrie Campbell (NIST) – ChiMaD

094 – 1015 Discussion

101 – 1030 Break

Discussion Lead: John Beatty (ARL)

103 – 1100 Marco Fornari (Central Michigan Univ.)	- AFLOWLIB.org

110 – 1130 Surya	Kalidindi (Ga	Tech) – Mosaic of Microstructure/IGERT-CIF21

113 – 1200 Discussion

120 – 1230 Lunch

Discussion Lead: Bill Mullins (ONR)

123 – 1300 Tom Searles (MDMi) – Cast Aluminum

130 – 1320 Dongwon Shin (Oak Ridge	National Laboratory, ORNL)	– Cast Aluminum

132 – 1340 Jake Zindel (Ford Motor	Co.) – Cast Aluminum

134 – 1415 Discussion

141 – 1430 Break

Discussion Lead: Jim Warren (NIST)

143 - 1500 Jason Sebastian (Questek)	– Cast Iron

150 – 1530 Lou Hector (General Motors) – Advanced	Steel

153 – 1600 Lara Liou (GE Aviation)	- Integrated Computational	Methods for Composite Materials

160 – 1700 Discussion

1700 Adjourn	for the day

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Wednesday

074 – 0800 Check-in

Discussion Lead: Wayne Zeigler (ARL)

080 – 0830 Terry Wong (Rocketdyne) – Nickel RS FEP

083 – 0850 Mike Glavicic (Rolls Royce) – MAI Ti Modeling

085 – 0910 Rajiv Naik (Pratt & Whitney) – MAI Data Informatics, Data Standards, Modeling & UQ

091 – 0940 Discussion

094 – 0955 Break

Discussion Lead: Dennis Griffin (NASA)

095 – 1015 Ro Gorham (National Center for Defense Manufacturing and	Machining, NCDMM)	–

America Makes

101 – 1045 Richard	Serwecki (NASA) - MAPTIS

104 – 1105 Scott Henry (ASM International) - Computational Materials Data Network

110 – 1135 Discussion

113 – 1200 Lunch

Discussion Lead: Clare Paul (AFRL)

120 – 1230 Ben	Blaiszik (Univ of	Chicago/ANL)	- Globus

123 – 1300 Mike Groeber (AFRL) – DREAM.3D/SIMPL

130 – 1320 Sam Chance	(iNovex ) - Semantics for Deep Interoperability

132 – 1340 Harlan Shober (RJ Lee) - Big Data and	Semantic Technologies

134 – 1400 Tim Hawes (Decisive Analytics Corp) – Natural Language Processing

140 – 1445 Discussion

144 – 1500 Wrap up

1500 Adjourn

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Presentation	Summaries

Welcome	and Introduction

Chuck Ward, Air Force Research	Laboratory

The primary intent of the workshop	is to promote communication between the groups being

funded under	the Materials Genome Initiative that	had significant	interest	in the management	of	

materials data. The greater awareness is expected to enhance the likelihood	for success of the

individual	efforts and speed	the community’s understanding of how best to	proceed	in	developing an	

MII.

The Materials Research Society (MRS) and the Minerals, Metals, and Materials Society (TMS)

conducted a survey	of the community	in May/June of 2013 to gauge	attitudes and needs in materials

data. Over 650 people took the survey,	with 74% of respondents saying they would be willing to share

data if it were encouraged	as a condition	of funding or publishing. The community showed	the most

interest in gaining access to databases of some of the most basic and essential	materials data, such as

thermo-physical properties.

The survey asked participants to evaluate advantages of local versus centralized databases.

Centralized	repositories were perceived	by respondents to	offer advantages in	cost, an	ability to	share

one’s data, provisioning of uniform data standards, and	an	ability to	organize and	find	data. Local

repositories were perceived to provide advantages in ease of data input, time efficiency and flexibility in

accommodating new data	types.

The survey also asked participants to provide their thoughts on impediments to and motivators

for	sharing data. Most	of	the impediments can be attributed	to	concerns of ownership	of data or

external data	restrictions while technical issues such as file size and proprietary formatting were	also

concerns. Overall, participants saw the ability to contribute data to the community for reuse and

critique positively. The highest motivator was	to enhance one’s	own research visibility. Interestingly

respondents saw adding metadata as a motivator, presumably due to	the perceived	higher value	of data	

when it is	described more robustly.

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MGI Efforts in Infrastructure Development	at NIST

Jim Warren, National Institute of	Standards and Technology

The presentation provided an overview of NIST activities supporting the creation	of a materials

data infrastructure. The infrastructure could	start by defining the interfaces/APIs (Application

Programing Interfaces) with which user is able	to interact for	the deposit	or	retrieval of	data. Beyond	

an interface, the following are essential elements for creation of viable infrastructure:

•	 The establishment of repositories to	store the data

•	 Tools to enable digital capture of the information, preferably at the time of creation

(computation or	experiment)

•	 Tools to enable markup of the data	with sufficient metadata	to inform someone else how the

data was created, as well as various attributes that would help the user judge the quality of the

information

•	 Assignment of a persistent digital identifier (like the DOI (Digital Object	Identifier) for	journals)	so

the data can be cited and discovered by other

•	 The registration of the availability of the data into	some sort of “registry” to	enable discovery

without prior knowledge of the existence of the repository/specific data features

Additionally, creation of new policy and	standards as well as development of tools to	evaluate

the quality of	the data and tools to	enable data-driven	discovery will be required to manage and use

materials data to the greatest extent possible.

NIST is responding to the OSTP (Office of	Science and Technology and Policy) memos (Dr. John

Holdren, 2 Feb 14) by developing practices that will be of use to	the community including a Common	

Access Platform allowing access across multiple collections of information,	Research	and	Development

of Persistent Identifier (PID) infrastructure with	the Research Data Alliance (RDA), and R&D o Data Type

Registries with	RDA.

Finally, NIST is collaborating with the National Data Service (based at the National Center for

Supercomputing Applications) and is developing a set of best practices for Data Management Plans.

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Th Materials Commons: A Novel Information	Repository and	Collaboration	Platform for the	

Materials Community

Brian	Puchala, Glenn	Tarcea, Stravya	Tamma, Emmanuelle Marquis, John	Allison,	University of Michigan

Critical to	accelerating the pace of materials science and	development is the development of

new and	improved	infrastructure providing a seamless way for materials researchers to	share and	use

materials data and models. To address this need, we are developing the Materials Commons

(http://prisms.engin.umich.edu/#/prisms/materialscommons),	an information repository and

collaboration platform for the metals	community	in selected technical emphasis	areas. We envision the

Materials Commons becoming a continuous, seamless part of the scientific workflow	process.

Researchers will upload	the results of experiments and	computations as they are performed,

automatically where	possible, along with the	provenance	information describing the	experimental and

computational processes. By	associating this	data with the experimental and virtual computational

materials samples from	which it is obtained, the Materials Commons will build process-structureproperty relationships enabling the construction	and	validation	of constitutive and	process models. The

Materials Commons website provides an easy-to-use interface for uploading and	downloading data and	

data provenance, searching, and sharing data. At its core, the	Materials Commons consists of secure	

data storage cluster, an	application	for efficiently uploading and downloading large data sets, and a REST

(REpresentational State Transfer) based	API to	access and	extend	the capabilities of the repository. The

API allows for features such	as automated	data upload	from experiments and	computations, seamless

integration of	computational models, and algorithmic analysis of	process-structure-property

relationships. The Materials Commons is a central thrust	of	the Center	for	PRedictive Structural

Materials Science (PRISMS).

NIST Materials Data Informatics Efforts

Carrie Campbell, National Institute of Standards an Technology

The presentation covered NIST’s pilot efforts in	creating a data infrastructure, focusing on

Phase-Based	Property Data. Phase-based data	are	diverse	data	that	include 1, 2, 3, and 4-dimensional	

data, are	semi-structured and often include incomplete data sets. The figure below provides an

overview of NISTs data efforts supporting the MGI.

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NIST Materials Data Repository (https://materialsdata.nist.gov/dspace/xmlui) is a customized

DSpace repository for materials that enables sharing of variety of data types, including text	files, images,

and video. The repository also provides persistent identifier for each entry and allows users to choose

from a variety of	license types. NIST is also working on two data curation tools for phase-based	data.

The Thermodynamic Research	Center (http://trc.nist.gov/) is extending the ThermoML markup language

and Guide	Data	Capture	software	tool to metallic systems,	with an initial emphasis on phase equilibria

and thermochemical data. The	Materials Data	Curation System (MDCS)	employs user-defined	XMLbased	schemas to curate	data. The	MDCS	allows either web-base data curation	or using REST API. The

entered data	is stored in a non-relational database repository, can be searched using SPARQL

(SPARQL Protocol and RDF Query Language) interface,	and integrated with variety of scientific

workflow	tools.	

AFLOW Consortium

Marco Fornari, Michigan	State University

The overall goal of the Materials Genome Initiative (MGI) is to accelerate the discovery,	

development, and	deployment of new materials with ad hoc functionalities. The AFLOW Consortium

focuses on the software infrastructures to achieve the MGI goals by using first	principles calculations.

The emphasis is on the data	that must be generated, archived, validated, and post-processed	efficiently	

both	to	identify new materials and	distill meaningful quantitative relationships within	the data. This

extends the	solution to the	inverse	problem of chemically replacing	rare	and costly elements in critical

technologies.

The AFLOW Consortium provides access to the repository AFLOWLIB.ORG

<http://AFLOWLIB.ORG>	that publicly distributes data	on energetics and thermodynamic stability for

more than 600,000 compounds and band structures for roughly 300,000 materials. The repository

includes bulk crystals and alloys.	Data were computed with the AFLOW high-throughput	framework

using density functional theory. The results of the calculations can	be queried	with	the recently

developed	REST-API that standardizes and	labels the data for retrieval purposes. The	fields defined in

the AFLOW REST-API include a material object identifier (AUID), data cloud	location	(AURL), ownership	

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and sponsorship keywords, and data	specific labels (COMPOSITION, ENTROPY, EGAP, ...). Simple	

scripting languages	can be used to automatically download	from the AFLOWLIB	repository. The AFLOW

approach has	been extensively applied on Platinum-group alloys, thermoelectric materials, magnetic

materials, topological insulators, scintillators, etc. The AFLOW REST-API can	be extended	to	include

experimental data. For further details on the	database, the	reader is directed to:

http://materials.duke.edu/auro/AUROARTICULA/j.commatsci.2012.02.002.pdf

Materials Innovation Infrastructure: Lessons Learned and Future Plans

Surya Kalidindi, Georgia Institute	of Technology

central activity in	materials research	is the exploration of processing-structure-property (PSP)

linkages.	 Workflow is defined	as the sequence	of steps employed for establishing	PS linkages of

interest to any specific engineering/technology application.

High Throughput Explorations of PSP Linkages:	 We must treat	 hierarchical	 material as a

complex System, which means embracing and exploiting uncertainty quantification	and	complexity

management concepts/tools.	 It also means we need to design, develop, and	validate decision support

systems	that will leverage the best current understanding (with	its uncertainties) and	provide objective	

guidance	on future	effort investment (combination of experiments and simulations).	 This means

leveraging Data Science to effectively feed the decision support systems described above by developing

and deploying high	throughput strategies for obtaining high value information from both experiments

and models.

What are integrated	workflows? Integrated workflows are those that utilize the best	

combination of experiments	and simulations	in extracting robust and reliable PSP linkages.	This requires

engaging and exploiting cross-disciplinary expertise that includes materials science, manufacturing,

systems approaches, uncertainty quantification, computational science, data and information sciences.

Through this, one must ensure that the workflows output the critical information needed	by design	and	

manufacturing stakeholders in the materials development value chain.	Integration demands expertise

sharing,	which essentially is the core element of a collaboration. Cross-disciplinary Integration	demands	

effective	collaborations,	such as those one might set up using the modern tools of the day,	i.e., a eCollaboration.

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Motivators for e-Collaborations:	 e-Collaborations are central to	enhancing the productivity of

materials development activities in several ways:

• individual	research groups can identify and engage	complementary expertise	needed for their

projects

•	 organizations can	coordinate multiple teams and	multiple projects to	maximize output/impact

•	 organizations can	identify and	seek the right partners that	enhance their	individual and combined

value.

e-Collaborations can	be open, semi-open, or closed	depending o the needs of the specific organization.

Productivity enhancement leading to dramatic reductions in cost and time	expended in materials

development is the	main incentive	for e-collaborations.

Main Impediments to e-Collaborations:

Lack	of tools to identify	the	“best”	collaborators

•	 Need software tools that quantify more precisely the expertise of individuals, groups,

organizations

•	 Need software tools for	allowing rich annotations and e-recording of	discussions. Need analytic

tools for	identification, extraction,	and management of metadata (i.e.,	high	value abstractions

from the raw data)

•	 Need searchable, semantic, metadata databases (these are	lightweight and can be	centralized

and customized for specific	user communities)

•	 Need software tools for tracking provenance (i.e.,	who/what/when/how	generated, modified,

etc.)

Lack	of experience	with the	use	of integrated workflows

•	 Need software tools	for e-recording of	workflows and development of searchable workflow

databases

•	 Need recommendation schemes that suggest the best workflow(s) based on recorded prior

experience; this provides means to transfer expertise	acquired by an organization to other

teams and other	projects (even in the same organization)

• Leads to possible automation and dramatic enhancement of productivity

MINED Group’s Past Experience:	(~2005 - ~2008): Funded by the Army Research Office, ARO,

(David Stepp); engaged computer science (CS) graduates to produce	a single	user platform that	

integrated certain software codes previously produced	by our group; software development

subsequently abandoned, but recruited two CS graduates into PhD programs in Materials Science and

Engineering/Mechanical Engineering (MSE/ME),	which dramatically altered the direction of	research in

the group. brief chronology of development:

•	 Senior Design Project (2011-2012): An online	portal designed for sharing data	files and tools;

learned the importance of user interfaces and the need for professional	code developers and

software architects.

•	 HUBzero Instantiation (2012-2013): Built hub by customizing HUBzero for hierarchical structural

materials; there are several advantages in terms of accessing and executing codes	(on the server

side), but there are significant barriers	to establishing fast and productive	cross-disciplinary ecollaborations.

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•	 GTRI Integration Platform (2013-2014): Engaged professional code	developers and software	

architects to design	a multi-user web	portal that allows data and	code sharing, while tracking

provenance and workflows; underestimated the resources needed for such a project.

•	 MATIN Collaboration Environment (2013-2014): Lightweight integrated software	shell that allows

users to	seamlessly interface with	numerous cloud	services offered	through	the web	through	

their	APIs. Examples include GitHub for versioning and archival of codes, Authorea for

collaborative editing of documents, figshare for citable publication of research, Plot.ly for

collaborative data analysis	and visualization, Google+ and LinkedIn for eteaming	and networking.

MATIN focuses on building an emergent community – Materials & Manufacturing Informatics

(MMI). MATIN provisions web services for	software, integration platforms, and innovation

infrastructure.

Examples of MATIN:	

•	 Spatial Statistics: https://github.com/tonyfast/SpatialStatisticsFFT

•	 PyMKS: http://openmaterials.github.io/pymks/index.html

•	 Example discussion about periodic boundary conditions in running Finite Element (FE or FEM)

simulations: https://github.com/sfepy/sfepy/issues/267

In the platform developed, it is noted that the user or user’s organization can set permissions at the

level	of individual datasets/codes o the degree and	type of	sharing.

Central Challenge: Activation. The transition from traditional disciplinary workflows to the

integrated workflows needs to overcome an activation barrier. There is no	clear incentive to	get ahead	

of the curve in	anticipating how the upcoming transformations in how materials development will be

pursued	in	the future. Open (reproducible) science alone is not a sufficiently strong motivator for

materials development community with	strong interests in intellectual	property development;	indeed, it

might actually	be perceived	as a negative. There is need to explore	and refine	what incentives work for

individual	researchers (e.g., increased productivity).

Future	Plans:	 Continue to	develop	and	deploy MATIN at GT and any other organizations/groups

that	express an interest in working with us. Design, create, and distribute	 suite	of “mobile	first” Apps

designed	to	make data ingest into	collaboration environments such as MATIN from both material

characterization equipment and multi-scale models an enhanced	user experience with	friendly and

natural interfaces. Proactively promote/incentivize	data/metadata	ingest into publicly shared

databases. Work with	professional societies to	announce and run new competitions/awards for	

recognizing and rewarding good practices by the members of the broader materials innovation	user

community (e.g., Best	Documented Data Award, Best Documented Tool Award, Best Documented

Workflow Award). Develop and deliver new workshops for training the new generation of	graduate

students, postdocs, and junior faculty in the	emerging workflows of Materials & Manufacturing

Informatics.

High Performance Cast Aluminum Alloys for Next Generation Passenger Vehicle Engines

Dongwon Shin, Materials Science and Technology Division, Oak Ridge National Laboratory

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Oak Ridge National Laboratory (ORNL) teamed with Chrysler and Nemak	to develop nextgeneration high-performance cast aluminum alloys that have	improved castability, high-temperature

strength, and fatigue	performance. At this point, the	data management practice is limited	to	analytical

data spreadsheets, other than	the CALPHAD computational thermodynamic databases and	proprietary

property databases for casting simulation. As the project goes on, more robust data schema will be

developed	to	store various types of data populated	from the experiments and	calculations.

Besides the data structure design, the speaker discussed	the necessity of a new approach	to	

identifying key descriptors from the large dataset that can guide the design of new materials. An alloy

design	requires the balance of extremely complex relationships among a large number of descriptors.

Some	examples of these	descriptors include	phase fraction	of key intermetallics, lattice parameters of

the matrix, and diffusion	kinetics	of solute atoms. However, optimizing these quantities relies on good

physical metallurgy intuition	coupled	with	clever experiments, dogged	sleuthing, and	a healthy dose of

luck.	Such descriptor-based predictions of materials properties date back to	the beginning	of modern

solid-state chemistry and physics. As	shown in the Darken-Gurry map to predict the solubility of

elements in Tantalum,	intuitive descriptors,	such as electronegativity and radius,	could be used for

materials design. However, as the chemistry and physics	of materials become more complex, intuitionbased	search	may not work anymore. Thus, the use of more advanced	statistical analysis and	data

mining approach might be required to accelerate the design of complex multi-component/multi-phase

alloy design.	

Data Related to Cast Aluminum

Jake Zindel, Ford Motor Company

The main focus of our research is the development of CAE (Computer-aided Engineering) tools

to support	two significant	activities in the company: component	design (product	development)	and

manufacturing engineering (high-volume production). A key	part of our research is conducting	

experiments to collect data	for the	development and validation of	the CAE tools.

The data	we collect consists of mechanical properties, physical properties, microstructural

images, and manufacturing process parameters that form the	pedigree	of the	material. This information

can be in the form of Excel files, proprietary-software storage files, screen shots, and written reports.

Our immediate need is to deal with this large amount of existing information, collected over

many years, on a “local” level. The first step is to organize many independent collections of information

in all	their forms.	 The next issue is the time required to enter all	this information into the database.	

Maintenance may also require a significant amount of effort. Lastly, there will also	be a need	to	

manipulate the data contained in the database using Excel, Mini-tab, MATLAB, etc. Therefore links or	

easy exportation of data	to these	types of applications will be	necessary.

The next level would involve dealing with information that has been created or collected by

others. From an	OEM (Original Equipment	Manufacturer)	perspective, this would	involve convincing

suppliers	to share processing information that they may consider proprietary. In addition to suppliers,

universities and	national laboratories are also	sources of data. Data from all the	sources will require	

some sort of protocol to determine its	quality and resolve inconsistencies.

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In the end,	it seems likely that considerable budget and time will be required to implement

comprehensive information/databases, which	could be the largest hurdle	of them all.

QuesTek’s MGI/ICME Approach to Alloy Optimization and Design (Including Cast Iron)

Jason Sebastian, QuesTek Innovations LLC

Integrated Computational Materials Engineering (ICME) methods involve the holistic application	

of different computational models across various length	scales to	the design, development, and	rapid	

qualification	of advanced	materials. Using its Materials by Design technologies, QuesTek has

successfully applied these ICME methods to	the design, development,	and full qualification of several

advanced alloys (including aerospace	structural steels, aerospace	gear steels, advanced castable	

titanium alloys, high-strength, corrosion-resistant	aluminum alloys,	and high-performance cast iron).

QuesTek’s ICME methods benefit tremendously from the activities of the U.S. Materials Genome

Initiative (MGI).	 Recognizing that the “10 to	2 years” that	it takes for alloy design and development is

hindering the rapid	deployment of higher-performance materials,	the MGI is focused on “cutting in half”

the time and cost	to design and deploy novel, advanced materials needed to enhance and sustain U.S.

competitiveness.

QuesTek also has ongoing	activities on the	application of ICME methods to the	design and

development of materials for the energy sector, including advanced	materials that enable vehicle

lightweighting.	 Of particular note is an ongoing effort with a major diesel	engine manufacturer on the	

development of high-performance cast iron	alloys for engine block applications. QuesTek’s efforts have

made extensive use of various MGI-type tools and databases including: 1)	density functional theory

methods for high-throughput	screening of cast iron inoculant stabilities (structure, lattice parameter,

stability of various	oxide/sulfide phases), 2)	liquid iron mobility databases (to help accurately model

solidification phenomena), and 3) thermodynamic	databases	(to examine nano-precipitate stability).

Moving forward, to continue to accelerate the materials development cycle, efforts should

remain focused on the development	of	the fundamental databases (thermodynamic, kinetic, etc.)	that	

enable	ICME models. There	should also be	increased focus o efforts related	to	the integration	of the

various computational databases, models, and tools (the “I”	in ICME).

Integrated Computational	Materials Engineering (ICME) of Generation Three Advanced High Strength

Steels

Louis G. Hector, Jr., General Motors R&D Center

While traditional material development methods often involve laborious trial-and-error testing,

Integrated Computational	Materials Engineering (ICME), which falls under the umbrella of the Materials

Genome Initiative, is offering a means	for materials	development under a more compressed time scale.

Advanced	high	strength	steels (AHSS), which	contain	multiple phases (e.g., ferrite, bainite, pearlite,

austenite, martensite), and may exhibit	phase transformation with strain, are fertile ground for	ICME.

However, prediction of macro-scale constitutive behavior based upon the multi-scale physical, chemical,

and mechanical phenomena	in these	complex microstructures is formidable challenge for ICME.

Unprecedented collaboration between universities, industry, and government labs will be required to

14
address AHSS	development, archiving of data, and the	implementation of these	materials into products

that	benefit	the American consumer. This presentation began with a brief overview of a new,

DOE/USAMP (US Automotive Materials Partnership)-funded ICME program aimed at	the development	

of Generation	Three (Gen	3) AHSS. The lack of commercial Gen	3 steels that meet DOE targets is offering	

significant challenges	to this	new ICME program. The program is	meeting these challenges	following four

thrusts: (1)	making Gen 3 steels to meet DOE targets; (2) passing experimental measurements of key

properties to	an	ICME model that produces microstructure-based	constitutive models; (3) material

evaluation in forming	and vehicle	performance	simulations; (4) archiving	all relevant data	for easy use	by

others in	the program. Although	the Gen	3 AHSS ICME program is “mission	oriented,” it demonstrates

an important role for universities in providing the necessary foundation in fundamental materials

science and engineering.

Integrated Computational	Methods for Composites Materials (ICM2):	Lessons Learned from

Integration Planning and Feasibility Demonstrations

Lara Liou, GE Aviation

General Electric Aviation and Lockheed Martin Aeronautics have teamed through an Air Force

Research	Lab	program to	conduct a demonstration	of an	Integrated	Computational Materials

Engineering digital framework that links material processing, property, and	structural relationships to	

account for processability, manufacturability, and system performance	with the	goal of demonstrating

that	the usage of	integrated models can contribute to future airframe and engine designs with dramatic

reductions in development	time and cost. Both engine and airframe Foundational Engineering Problems

will be demonstrated through a common digital framework to be developed and validated on

increasingly complex articles.	 The modeling tools that	cover composite process models, localized	

mechanical behavior models, and global design models (Convergent Manufacturing Technology’s

COMPRO CCA, Autodesk’s Autodesk Simulation	Composite Analysis, and	an	Abaqus based	University of

Michigan micromechanics model) are centrally linked via a commercially available model integration

interface (Phoenix Integration’s ModelCenter®).	Detailed plans of what data will	be passed among the

various models as well as how it will be passed have been completed. Creation of these detailed	

process maps and	simple feasibility demonstrations have raised	inherent technical and	logistical

challenges	to integrating commercially	available, multi-scale models. Robust model integration methods	

to address these challenges were	discussed.

The exercise of drawing up detailed process maps for what to integrate and how to integrate	

the selected tools and models resulted in key early lessons learned and identification of	tools critical to

long-term, robust, and sustained application of	Integrated Computational Methods for Composites

Materials (ICM2).	 The key lessons learned include:

• Individual	models must be relatively mature and validated before an ICM2 problem can be

confidently	defined.

• F analysis-based	tools within	the same digital thread should utilize	the	same	FE solver.

• The team, ideally,	should consist of at least one expert for each	tool and	its underlying model as

well as an expert in the digital thread and integration interface tools and programming.

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• Mesh interpolation between	finite element analysis based	tools will likely be required, and	a

commercially	available tool for this	type of composite mesh interpolation is	key	for robust and

sustained application of ICM2.

Residual Stress Engineering of Rotating Nickel Components

Terry Wong,	Aerojet Rocketdyne

The goal for the PW-8 project:	 The Nickel Base Superalloy Residual Stress Foundational

Engineering Problem program aims to	develop	a multi-disciplinary analysis approach	to	predict and	

incorporate bulk residual	stress in the	design of component. This will allow for design of part that

is optimized for a particular application or design intent (such	as weight, performance, or reduction	of

scrap). To accomplish this	goal, the PW-8	team has two major tasks; 1) the	development of an	

infrastructure and associated tools needed to predict and incorporate bulk residual	stress in the design

stages	of a part, and 2) a demonstration task that will validate the infrastructures	and tools	developed.

The Data	Management task is the foundational element	of	the larger	infrastructure

development task. The goal for the data management work is to	develop	an	infrastructure that allows

the various team members in PW-8	to share	data	and to store	data	in database. This not only ensures

that data generated	by this program is never lost but also	allows for data to	be transferred	in	a secure

manner. In order to meet the goal of a system	that securely stores and shares data, three major tasks

must be accomplished: 1) Validation that there	is secure	method to transfer data	between team

members and that the method of storing data is also secure, 2)	 development of	a database schema and

3) development of means to easily upload data	into the	database	and download data	from the	

database.

Of the three, the most difficult task was to create a system in which the transfer and storage of

data was secure. Various proposals were made to	ensure the security of the data but many of them

were rejected, not because the proposal was actually unsecure, but because it was not acceptable by

each of the	team member’s IT security policies. The	process to get each team members’ IT department

to approve the security of	the data transfer	and storage took more effort	than anticipated. In the end,

the team members along with their	respective IT departments approved the use of	TRUcentrix as a tool

to transfer	data and the use of	an ASM International	hosted	database to	store data.

The development of database schema	is necessary step in creating the database to store all

the data generated (via physical tests as well as data generated by models). A big help in creating the

database schema was to	use the schema generated	by Dr. Steven	Arnold	from NASA’s Glenn	Research	

Center. Using NASA’s schema eliminated	the need	to	start the database build	from scratch	and	allowed	

us to	focus o the specific needs of storing residual stress data.

The last task involves the development of tools that make the input of data	easy. The more

difficult it is to	enter data into	a database, the more likely data	will not be	entered. Excel-based	

templates are being developed in order	to make data entry as easy as possible.

The two big lessons learned from PW-8	in relationship to data	management are:

•	 It is essential	to work with each	companies’ IT and IT Security people to develop a system that

satisfies	the security requirements of each company.

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• Database schema development should not be reinvented but begin with already established the	

schema.

Perspectives on MAI Programs

Dr. Mike Glavicic, Rolls Royce

The current formulation of MAI (AFRL’s Metals Affordability Initiative) programs are structured	

independently from one another in that a standard methodology does not exist that describes what

data is to	be archived	at the conclusion	of a program. Nor does there exist a standard	protocol of how

to archive things on a server	or some other computer resource at the conclusion	of a program. As a

result, the current	practices of	MAI programs:

• Do not address IP (intellectual property)	and licensing issues in systematic fashion, which leads

to issues in the commercialization of ICME software that is generated	under a program

• Do not have a formalized requirement of how data is to be packaged and stored at the end of a

program. Hence even	in	the best of cases in	which	all of the data is stored, the data is archived	in	

simple folder	fashion that	is not	searchable in any manner	possible. Moreover, even if	all of	the

data is present it is extremely cumbersome to	untangle the data for future use using a file folder

strategy.

Hence at the end of the program both the government and	MAI team are	left to manage	the	

data and	results in	an	unregimented	fashion.

The lack of regimented protocol of how to package program’s	results	(raw data and reports)

makes it extremely difficult for new government programs to leverage the data from previous programs.

Case in	point, the RR-10	program was developed with the	singular goal to integrate	the	models

developed	in	the LAD-2	program into commercial software	platform. From the	outset of the	program,

data, models and	other information	that was required to attained the	goals of the	RR-10	program were	

either difficult to track down, lost or not provided to the	RR-10	team due	to contractual disputes. As a

result, funding that	was earmarked for	specific technical work at	the conception of	the	program had to

be diverted	for the recreation	of data that existed	in	a previous program and	the progress of the

program was stunted	by this unforeseen	burden. In	spite of the RR-10	program’s	ability to overcome

these issues and become a successful program, it	should be noted that	the success of	other	programs

that	will encounter	similar	problems will likely not	apply as RR-10	was very fortunate	to have	the	means

to overcome these serious issues that	were a result	of	the lack of	a data management	plan.

Furthermore, if the	MGI initiative	is to progressively develop materials models that are	

leveraged and improved in future government funded programs a policy on how to store and share the

data from disconnected	programs is required. The PW-11	program is the first	step in this process as it	

will have the data from three MAI programs (RR-10, GE-10, PW-9) uploaded into database	that will be	

searchable and will enable the reuse of the data collected in these programs. However, even if this	

program is a success	there will undoubtedly	be other features	that will be required in the platform

developed	and	hence future modification	will be required. Most importantly there does not currently	

exist server that has been set up and funded for the	archiving	of all	future programs with a protocol	

17
						

that	must	be adhered to at	the conclusion of	a funded program. Without	such a protocol and the

existence	of server to be	used in all future	programs, the	goals set forth by MGI will be	unnecessarily

difficult and	more expensive to attain.

MAI Data Informatics, Data Standards, Modeling & UQ

Dr. Rajiv Naik, Pratt & Whitney

The MAI Data	Informatics, Data	Standards, Modeling & Uncertainty Quantification (UQ) (PW-11)	

effort	will enable the pervasive implementation of ICME by developing (i) a web-based	OEM-Supplier

collaboration framework	for production	certification	and	process modeling data, (ii) data standards and

protocols for interoperability between	OEMs and	Suppliers, and	(iii)	UQ methods to capture variability in

process, microstructure, and property data. Models and data	from earlier MAI programs such as GE-10,

PW-9	and RR-10	will be	used to develop and implement OEM-Supplier integration, data standards, data

structures, and UQ models. Several OEMs and suppliers (Pratt & Whitney, General Electric, Rolls-Royce,

TIMET, Alcoa	and RTI International Metals, Inc.) are working together on the PW-11 Team.

The OEM-Supplier collaboration tool will enable data	exchange related to mechanical testing,

microstructural	characteristics, processing, and process modeling. The	data	standards developed by the

PW-11	team will provide	an industry standard for such OEM-Supplier collaboration. The collaboration	

framework would provide a platform to manage OEM-Supplier	workflows, data and	information,

security and access	control, and the	ability to perform system administration without the	need	for day

-to-day intervention	of the IT department. The capture of such	supplier data over the collaboration

framework will enable the OEMs to enhance process and property	models, data analytics, and UQ

modeling. Three potential technologies will be evaluated	for the OEM-Supplier Collaboration	Framework

– GE Forge, Vanderbilt Forge, and Semantic Web technology and one	technology will be selected	for the

next phase of the program.

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Semantic Web technology is paradigm shift from highly structured	databases to	more flexible

graph databases, from single database searches to	multiple and	disparate	database	searches, and from

keyword searches to contextual and meaningful (semantic) searches. It can provide flexibility	of data

structures and enhanced OEM-Supplier	interoperability. However, it requires more work upfront	for	

implementation as it needs the development of a common vocabulary and relationships.

The data	standards and data	structures effort will leverage work at national and	international

standards	organizations such	as ASM, ASTM, CEN, and	NIST and	develop	standards and vocabularies for

production	certification data that can include chemistry, microstructure, and mechanical properties.

This effort will also develop means for the capture of data needed	for the UQ and	ICME models and

build	databases for	the capture of	the data from the GE-10, PW-9, and RR-10 MAI programs.

The ICME	models being developed under the GE-10, PW-9, and RR-10 programs involve several

interconnected models related to processing, microstructure and mechanical properties. The UQ effort

under PW-11 will involve considering the uncertainty at each	step	of the modeling process and flowing it

through the interconnected models to evaluate the relationships between	the variabilities of	the

processing parameters, the microstructure and	the mechanical performance.

ASM’s Computational Materials Data Network & the NIST-ASM Structural Materials Data

Demonstration Project

Scott D. Henry, ASM International

ASM International – the world’s largest	association of	metals-focused materials engineers,

scientists, technicians, educators, and students	– has launched	a series of initiatives under the

“Computational Materials Data (CMD)	Network” umbrella. The objective	of the	CMD Network is to serve	

as center for information collection and dissemination for materials data	to support integrated

computational materials	engineering (ICME) and to help realize the goals	of the U.S. Materials	Genome

Initiative.

The first major project for the CMD Network is the NIST-ASM Structural Materials Data

Demonstration Project (SMDDP), a cooperative research project funded by NIST with participation from

the Kent	State Center	for	Materials Informatics and Granta Design Ltd.	The main	objectives of SMDDP

are	to:

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• Establish well-pedigreed	and	curated	demonstration	datasets for non-proprietary metallic

structural materials	data over multiple length scales. The team has	chosen to work with 6061

aluminum and the	related Al-Mg-Si system.

• Work with NIST and the materials data community to develop materials data schema and

ontologies for the demonstration	datasets, cognizant of broader interests and	datasets.

• Develop and carry out a series of test problems that represent relevant use cases for the	

repository.

• Make data open to the materials data community for use in data analytics, modeling, and

educational purposes.

• Actively engage the materials data community and	widely disseminate the findings from the

project.

• Develop and implement data	capture	and curation procedures that can serve	as models for other

data repositories.

The basic philosophy behind the project is that often standards	emerge through trial and error,

and examples are	needed to spur this process.

The project is using the NIST-KSU DSpace	repository for data	files. The	curated, structured

database is being developed	using GRANTA	MI software and	is hosted	o ASM servers.

The project –launched in late 2013 and planned as an 18-month program	– is being conducted in

phases. The	first phase	involved developing starter database	and gaining experience	in data	curation.

The second phase (currently underway) involves building out more comprehensive data	sets that will be

suitable for carrying out test problems	related to processing-structure-property relationships in	Al 6061.

The third phase will involve making further refinements in response to testing. The complete data	set

will be opened up online to the materials community as a whole.

Globus	Scientific	Data Services:	Current and Future

Ben	Blaiszik1 Kyle Chard1 Jim Pruyne1, Rachana Anathakrishnan1, Steve Tuecke1,2, Ian Foster1,2

1

University of Chicago Computation Institute, 2Argonne National Laboratory MCS Division

During this talk, we discussed a variety of currently available Globus services.	Via these Globus

services, developers	and IT administrators	can offer simple, secure, and robust file transfer, as	well as	

identity, profile, and group management to their user communities.	Globus allows end users to create

and	manage a unique identity that can	be linked	to	external identities for authentication. End	users can	

also transfer files across wide	area	networks faster and more	easily, whatever their location.

The talk also covered prototype services related to data	publication, metadata tagging, and	data

search. Globus	publishing capabilities	are delivered through a hosted service. Metadata is	stored in the

cloud, while raw published data and a fully-formed metadata log are stored on campus, institutional,

and group resources that	are managed and operated by campus administrators. Published datasets are

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organized	by "communities" and	their member "collections". Globus users can	create and	manage their

own	communities and	collections through	the data publication	service	with collection level policies

regarding user	access. Additionally, metadata from these communities are imported into a centralized

index to allow for robust searching functionality.

Semantics	for Deep	Interoperability

Samuel Chance, iNovex

Semantic technology is based on mature	web standards and intended to be	used as global

framework for	machine-to-machine communication. Interoperability is enabled even when the more

basic elements of the semantic technology stack are used	in	conjunction with a controlled vocabulary.

Knowledge	representation is enabled as one	begins to utilize	the	“higher” elements of the	stack.

brief history of the semantic web	was presented	and	started	with	a depiction	of the web	that

most people see today: documents and web pages presented to the user	through a web application. It	

then depicted a transition to an intermediate state where documents and data can co-exist on a

semantic	web riding atop the same infrastructure (http:). Even in this	state, the data can	be directly

interpreted by a machine.	 The history concluded with a depiction of data being extracted from a

document to	become part of the web	of data.

The basics of semantic technology were presented and emphasized that the technology has a

foundation based	o formal logic that enables software	to understand information and reduce	the	need

for	a human to interpret	the meaning. This section of	the presentation concluded with a chart	depicting

how “triples” link to	each	other to	form a web	of data.

The final section touched upon adoption of	and things to consider	when semantic technologies

are	being considered as solution for community or enterprise.

21
Big Data	and Semantic Technologies

Harlan Shober, RJ Lee Group

The presentation started with a description of entities that	comprise laboratory

informatics with the central elements being a federated architecture, scientific “Big Data”

management	and workflow-driven Laboratory Information Management	Systems (LIMS).

To aid attendee understanding, initial context	was provided by first	defining for “Big Data” and

Semantic Technologies. Context	was further bolstered through a description of a problem that	

included laboratory testing/characterization and fabrication processes. The challenges for a

typical laboratory include the use of many systems and the creation of large amounts of data	

that	grows daily but	cannot	be readily accessed.

After the context	was established, the needed functionality was described and consisted

of several elements: contribute, index, search, analyze, and archive. While bearing in mind the

needed functionality, a string of universal scientific data	management	process categories were

then presented with the intent	of applying the universal description to the materials design and

development	domain.

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Though semantic technologies are applicable over the entire lifecycle of the data	

management	process, three categories were specifically identified as primary candidates for

their application: Data	Acquisition, Data	Manipulation and Storage, and Discovery and

Advisory. W3C standards for Resource Description Framework (RDF) were applied to a notional

example with the intent	of completing a thread that	gave the attendee a sense of how this

technology could be applied.

The presentation continued with a description of Postulate, a specific solution

developed by RJ Lee Group that	could be used as part	of a suite of tools to enable the capture

of data	and the near-frictionless tagging of data	with additional observations, provenance

information, or links to additional data. Using tools like Postulate, repositories could be more

easily shared with others in a federated manner. These technologies would enable the

development	of indices that	increase the precision of search results and ease analytics since the

data	would be “self-described.”

In the remainder of the presentation, Postulate functionality was further described, and

some of the challenges presented earlier were addressed. For example, how do you process

the quantity and variety of data, how do you store it, how do you find the data	you care about,

and how can the data	be presented to you in a meaningful way?

Natural	Language Processing (NLP)

Tim Hawes, Decisive Analytics

The presentation provided the attendees with a description of NLP	and how it can be	used. It

started with examples	of how it can be used to mine “Big Data” to predict credit card fraud or assess	

user purchase histories for product development. NLP is also	being used	in	the field	of biology and	

appears to have	enjoyed some	level of success.

Since	the	biology and materials communities both publish large	volume	of documents

(abstracts, papers, presentations, etc.), many of	the NLP approaches developed for	biology may be

applicable	to materials.

The next section of the presentation focused on describing various aspects of NLP: tokenization,

part-of-speech tagging, disambiguation, parsing, entity disambiguation, and role	labeling. This was

helpful in	removing the mystery surrounding NLP for those	who hadn’t had previous exposure.

Typical products from NLP	would be useful to the materials development community. Examples

include providing the gist of a document’s content, providing answers to natural	language questions,

and knowledge	construction.

Specific NLP	challenges for the	materials domain were	identified: inline	use	of equations,

prevalence of tables, graphs and	images (more of a computer vision	problem) and	numerically driven	

information.	 Additionally the “genre” problem was discussed as	well as	a way this might be addressed

using raw data (versus learning data) and	semi-supervised learning, active learning, and distant

supervision approaches.

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The presentation concluded by reiterating how NLP	can be used and the value of using it,

describing some its limitations, and	identifying tool development needed	for the materials domain.

24
Community	and	Government Priorities in	Addressing	Materials Data

The following numbered	priorities for	both community and government	actions were ‘rolled up’

from workshop participants’ inputs and were reviewed at	the workshop. The raw inputs provided by

participants are captured	under each	numbered	priority and provide context to	the priority and	often	

more specific actions.

Community-Led	Priorities

1. Develop	and deploy	standards	for data and federated/collaborative environments

- Promote	standards for data	and collaboration environments

- Designing and deploying suitable software environments that are capable of facilitating, erecording, and accelerating (fail often-fail fast)	of	the community’s experience (both successes and

failures)	in exploring integrated workflows needed to realize the vision of ICME and MGI

- Data	standards (program management/technical data/metadata)

- Community must start developing a common	data format. data "package" could	look like an	

HDF5 file and be shared in a variety of systems

- Begin	definition	of standardized	infrastructure

- Produce	draft guideline for the metadata including information regarding authorship, provenance,

target...

- Share	the	data	types being collected with the	community. Include	name/type/units of data

- Community must adopt a federated	approach	- allow organizations to autonomously create their	

own	toolsets, but be able to	read/write to	the common	data package mentioned	in	point 1.

- Good documentation on repositories are important

- Address networking of repositories (lightweight metadata database/index)

- Digital heterostructure microstructure mapping algorithms for both scanning electron microscopy

(SEM)	and transmission electron microscopy (TEM)

- Materials process model (thermodynamics) integration with ICMSE (Integrated Computational

Materials Science and Engineering) data flow

- Data sharing protocol across users (government,	OEM,	developers) with appropriate security

control

- Work with instrument/equipment OEMs to get auto ingest capabilities and open architectures

- Work together to define common vocabularies for interoperability

- Standard tests for ICME	model data	standards for microstructure	data

- Standards for ICME	model data	storage	and workflows

2. Communicate the value and need	for digital materials	data

- Brand	management – consistent messaging of goals	and beliefs

- Share	success stories

- Value (ROI,	Return on Investment) o data capture (globally, locally)

- Give incentive ($$$) for case studies to provide evidence that a shared data repository can be

actually useful beyond the archival purpose

25
- Share	ROIs for MII (the	Materials Innovation Infrastructure)

- ICME projects, due to their iterative nature, take longer and are more expensive to realize benefits

in industry implementation.	 Multi-year ICME programs are becoming	a hard sell to management,

difficult to	establish ROI and	realize benefits claimed.

3. Engage the community

- Write grant proposals

- Collaboration	(or e-collaboration) science that will help us	identify	the specific	incentives	and

impediments for establishing mutually beneficial, highly productive, sharing of expertise between	

disciplinary experts in	materials, manufacturing, and	data sciences

- Continue to	work o hard	problems—develop	open	source codes: discover new descriptors, data

mining, uncertainty analysis

- Organize as a community (government/industry/academia, national/international)

- Follow data	recommendations in MGI strategic plan—workshops to develop roadmap

- Attend	more events like the MGI workshop	— gaining	lessons learned from other organizations is

extremely valuable

4. “Define critical data”	to be	compiled

- Consensus o what fundamental genomic data is most-important to compile (thermodynamics?	

kinetics?	 properties [strength]?	 high-throughput	DFT?)

- Agree to	the concept of a “universal repository” for all genomic data that everyone will contribute

to and use (cf. everyone maintaining and hosting their	own databases); this includes issues of	data

security and sharing, etc.

- List of most-important ICME/MGI	materials development activities, from an industry/commercial	

perspective (alloys? composites? piezoelectrics? etc.)

- Some	ongoing repository efforts seem redundant. We	should avoid unnecessary competition and

try not	to step o other people's toes.

5. Explore and leverage data solutions developed	outside materials	community

- Semantic web infrastructure	– evaluate	feasibility of ‘unstructured materials data” management

(Technical path/cost/timeframe)

- Keep communicating outside	the	community about solutions

- Focus on available	tools, open ones when possible

6. Train	students

- Sponsor students	who can think in terms	of IT and MSE

- Training workshops (through professional societies) for training the new generation of materials

students	and researchers	in the use of readily (already) accessible data science tools	and cloud

services	to make	the	best use	of this emerging	infrastructure	in enhancing	their own individual dayto-day productivity.

7. Establish community	norms	for publishing materials	data

- Involve publishers by requiring data when publishing. Begin by making just the data available first

and then work on standardization panel

26
- Data associated with publications should be shared along with the publications. Not only the

metadata (e.g. average grain size vs cooling rate) but also the source of the data (e.g. micrographs

used	to	generate	grain size, temperature/time charts	used to calculate cooling rates, etc.)

Government-Led	Priorities

1. Lead	data strategy/approach	development

- Hold meeting like this one in 6-12	months with purpose	of developing roadmap for MII

- Define a technical roadmap for	the materials data infrastructure

- Government’s got to decide on a strategy

- What’s the critical nucleus? e.g., getting	CALPHAD off the	ground

- Government must establish an official stance on security and infrastructure.

- List of most-important ICME/MGI materials development activities, from a government perspective

(alloys? composites? piezoelectrics? etc.)	

- Needs assessment for professional workforce development

- Work with industry to make sure the research-to-engineering	gap is filled. What is the practical	use

of the projects

- Provide	the	materials community organizing function

- Business case for productivity improvement with	data readily available

- Facilitator as honest broker of capability awareness and data	sharing

- Interdisciplinary coordinated effort	to avoid/minimize duplication and redundancy

2. Facilitate data deposition (policy & technology)

- Setting expectations and guidelines for best practices and standards, where	relevant

- Invest in tool	making data management as easy as possible

- Promote standards development/common data structures/share knowledge representations

- Learn about the WBC Semantic Web capabilities, and limitations

- Government should foster community development for both data package refinement and creating

standard approach to creating an enterprise toolset.

- Provide	motivation for standard data	collection; ensure	it includes pedigree/provenance/meta-data

- Facilitator for materials data	standards (good and trustworthy data	vs. bad data)

- Facilitator for forming consortium for data	mining, data	warehousing, and sharing

- Facilitate	industry standards for ICME	model validation and maturity levels

- Facilitate	standards for model-based	process and	supplier qualification

3. Incentivize data deposition

- Provide	motivation for	standard data collection, ensure it	includes	pedigree/provenance/meta-data

- Push “data	management plan” requirements to “data	deposit” requirements

- Make data sharing a requirement

- Contractual requirements that preserve data for future use

- Demand data submission as a deliverable that will be in the contract before paying the funds.

- Require that any data generated	by federal grants is provided	to	the government (or a designated	

repository)	within 3 years after completion—start with basic	research

- Demonstrate the return on investments in the materials data infrastructure

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4. Support	data sharing	and deposition	(provide resources)

- Increase % of MGI	funds dedicated to MII	development to 10%--repositories & collab. platforms

- Commit to	long-term support for data repositories & collaborative platforms

- Funding for development / collection of fundamental genomic data

- Support thermodynamics/kinetics database development for light-weight alloy systems

- Funding for universal data	repository (e.g., at NIST) – creation and maintenance

- Fund or establish data	repositories with common/uniform data	collection format

- Create/identify secure data sharing site (like AMRDEC)	for	government	contractors—not a

repository

- AFRL take lead	o making a database that can	be	shared

- Infrastructure for materials related semantic web applications

5. Support	data creation

- Support experimental measurement effort to provide	quality data for	database development

- Support more	high-throughput	(HT)	studies	for applied programs	to populate data. Current HT

approaches support are	limited to 'too' fundamental single-phase DFT calculations

6. Enhance data management	competency

- Government needs to increase in-house expertise in	these areas, or alternatively, work closer with	

groups like NIST. We cannot allow other organizations to define our needs for us.

7. Provide quality datasets	for model	development/validation

- Need for open model benchmark cases with data to be available to the community.

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Attendees

John Allison University of Michigan

John Beatty Army Research Laboratory

Ben	Blaiszik University of Chicago/ANL

Carrie Campbell NIST

Sam Chance	 Inovex

Jennifer	Fielding Air Force Research Lab

Marco Fornari Central Michigan

Tim Hawes Decisive Analytics Corp

Lou Hector General Motors

Andrea Helbach Air Force Research Lab

Kris Hill ITI-Global

Mike Glavicic Rolls Royce

Ro Gorham NCDMM

Dennis Griffin NASA

Mike Groeber Air Force Research Lab

Scott Henry ASM International

Matt Jacobsen Air Force Research Lab

Will Joost Dept of Energy-EERE

Surya	Kalidindi Georgia Tech

Kevin Kendig Air Force Research Lab

Ben	Leever Air Force Research Lab

Lara Liou GE Aviation

Kenny Lipkowitz Office of Naval Research

Bill Mullins Office of Naval Research

Benji Maruyama Air Force Research Lab

Rajiv Naik Pratt & Whitney

Jim Neumann Honeywell

Ruth	Pachter Air Force Research Lab

Clare Paul Air Force Research Lab

Amra Peles United	Technologies Research	Center

Andy Powell GE Aviation

Brian	Puchala University Michigan

Ajit Roy Air Force Research Lab

Ayman	Salem Materials Resources

Tom Searles Materials Data Management

Jason Sebastian Questek

Richard	Serwecki NASA

Dongwon Shin Oak Ridge	National Labo

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Harlan Shober RJ Lee

Jeff	Sheehy NASA

Jeff	Simmons Air Force Research Lab

Veera SundararaghavanUniversity of Michigan

Sesh Tamirisakandala RTI

Glenn Tarcea University of Michigan

TJ Turner Air Force Research Lab

Vasisht Venkatesh Pratt & Whitney

Chuck Ward Air Force Research Lab

Jim Warren Natl Institute of Standards & Tech

Jeff	Williams GE Aviation

Terry Wong Rockedyne

Wayne Ziegler Army Research	Laboratory

Jake Zindel Ford Motor Company

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