NISTIR 7898

Building the Materials Innovation

Infrastructure: 

Data and Standards

James A. Warren

Ronald F. Boisvert

http://dx.doi.org/10.6028/NIST.IR.7898
NISTIR 7898

Building the Materials Innovation

Infrastructure: 

Data and Standards

James A. Warren

Materials Science and Engineering Division

Material Measurement Laboratory

Ronald F. Boisvert

Applied and Computational Mathematics Division

Information Technology Laboratory

http://dx.doi.org/10.6028/NIST.IR.7898

November 2012

U.S. Department of Commerce

Rebecca Blank, Acting Secretary

National Institute of Standards and Technology

Patrick D. Gallagher, Under Secretary of Commerce for Standards and Technology and Director
Building the Materials 

Innovation 

Infrastructure: 

Data and Standards

A Materials Genome Initiative 

Workshop 

Summary Report 

May 2012 Workshop

National	Institute	of	Standards	and	Technology	
Workshop Report:

Building the Materials Innovation Infrastructure: 

Data and Standards

A Materials Genome Initiative Workshop

Summary Report for Workshop held

May 14-15, 2012

Washington, DC

Prepared for the National Institute of Standards and Technology 

November 2012
Workshop Report: Building the Materials Innovation Infrastructure: 

Data and Standards

i

Acknowledgments 

This report summarizes the results of the Workshop “A Materials Genome Initiative 

Workshop – Building the Materials Innovation Infrastructure: Data and Standards” held 

May14-15, 2012, in Washington, DC. The workshop was a collaborative effort of the 

National Institute of Standards and Technology (NIST) and a representative Steering 

Committee (members shown below). Thanks are extended to the Steering Committee 

co-chairs and members for their extensive efforts in guiding this workshop and 

successfully reaching the outcomes. Thanks also go to Energetics Incorporated for their 

assistance in facilitating the workshop and preparing this report.

Steering Committee Co-chairs

James A. Warren, NIST

Ronald Boisvert, NIST (co-chair)

Steering Committee Members

John Allison, University of Michigan

Cynthia Friend, Stanford Linear Accelerator Laboratory

David Furrer, Pratt & Whitney

Kristin Persson, Lawrence Berkeley National Laboratory

Cover photo credits (from left to right)

NIST – L. Bendersky, 2011. 

Microsoft Online – MH 900178448, 2012.

NIST – B. Young, 2004.

Microsoft Online – MH 900402686, 2012.

National Renewable Energy Laboratory – D. Schroeder 2012, Research Support Facility.

Disclaimer

This report was prepared as an account of work cosponsored by NIST. The views and opinions expressed herein 

do not necessarily state or reflect those of NIST. Certain commercial entities, equipment, or materials may be 

identified in this document in order to illustrate a point or concept. Such identification is not intended to imply 

recommendation or endorsement by NIST, nor is it intended to imply that the entities, materials, or equipment 

are necessarily the best available for the purpose.
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Workshop Report: Building the Materials Innovation Infrastructure: 

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iii

CONTENTS

1 INTRODUCTION.................................................................................................................................................... 1

1.1 THE MATERIALS GENOME INITIATIVE ................................................................................................................ 1

1.2 WORKSHOP OVERVIEW...................................................................................................................................... 2

1.2.1 Workshop Process......................................................................................................................................................... 2

1.2.1.1 Data	Challenges	at	Length	Scales ......................................................................................................2

1.2.1.2 Data	Challenges	for	Technical	Application	Areas ..............................................................................3

1.3 ORGANIZATION OF THE REPORT ...................................................................................................................... 3

2 LENGTH SCALE CHALLENGES .......................................................................................................................... 4

2.1 MACRO LENGTH SCALES .................................................................................................................................... 4

2.1.1 Overview of Macro Length Data Challenges.......................................................................................................... 4

2.1.2 Top Short-Term Macro Length Challenges............................................................................................................. 4

2.1.3 Top Long-Term Macro Length Challenges.............................................................................................................. 6

2.1.4 Macro Length Challenges with both Short- and Long-Term Impacts.............................................................. 7

2.2 MICRO LENGTH SCALE........................................................................................................................................ 8

2.2.1 Overview of Micro Length Data Challenges........................................................................................................... 8

2.2.2 Top Short-Term Micro Length Challenges............................................................................................................10

2.2.3 Top Long-Term Micro Length Challenges.............................................................................................................10

2.2.4 Micro Length Challenges with both Short- and Long-Term Impacts.............................................................11

2.3 NANO AND MOLECULAR LENGTH SCALE ......................................................................................................12

2.3.1 Overview of Nano and Molecular Length Data Challenges.............................................................................12

2.3.2 Top Short-Term Nano and Molecular Length Challenges................................................................................13

2.3.3 Top Long-Term Nano and Molecular Length Challenges.................................................................................14

2.3.4 Nano and Molecular Length Challenges with both Short- and Long-Term Impacts.................................15

2.4 ATOMIC LENGTH SCALE ...................................................................................................................................15

2.4.1 Overview of Atomic Length Data Challenges.......................................................................................................15

2.4.2 Top Short-Term Atomic Length Challenges..........................................................................................................17

2.4.3 Top Long-Term Atomic Length Challenges...........................................................................................................18

2.4.4 Atomic Length Challenges with both Short- and Long-Term Impacts...........................................................19

3 TECHNICAL APPLICATION AREAS................................................................................................................ 21

3.1 ELECTROCHEMICAL STORAGE ..........................................................................................................................21

3.1.1 Overview of Data Challenges for Electrochemical Storage ..............................................................................21

3.1.2 Top Challenges for Electrochemical Storage........................................................................................................22

3.2 HIGH-TEMPERATURE ALLOYS ...........................................................................................................................28

3.2.1 Overview of Data Challenges for High-Temperature Alloys ............................................................................28

3.2.2 Top Challenges for High-Temperature Alloys......................................................................................................29

3.3 CATALYSIS ..........................................................................................................................................................36

3.3.1 Overview of Data Challenges for Catalysis...........................................................................................................36

3.3.2 Top Challenges for Catalysis.....................................................................................................................................37

3.4 LIGHTWEIGHT STRUCTURAL MATERIALS ........................................................................................................44

3.4.1 Overview of Data Challenges for Lightweight Structural Materials...............................................................44

3.4.2 Top Challenges for Lightweight Structural Materials.........................................................................................45

4 CROSS-CUTTING CHALLENGES..................................................................................................................... 53
iv Contents

TABLES

Table 2.1 Macro Length Scale Challenges

Table 2.2 Micro Length Scale Challenges

Table 2.3 Nano and Molecular Length Scale Challenges

Table 2.4 Atomic Length Scale Challenges

Table 3.1 Electrochemical Storage Challenges

Table 3.2 High-Temperature Alloy Challenges

Table 3.3 Catalysis Challenges

Table 3.4 Lightweight Structural Material Challenges

FIGURES

Figure 3.1.1 Define and Generate Data on Materials and Systems

Figure 3.1.2 Standardization of a Simulation and Analysis Toolbox

Figure 3.1.3 Central Repository and Software Infrastructure

Figure 3.1.4 Communication across Different Stages of Development

Figure 3.2.1 Data Retrieval for High-Temperature Alloys

Figure 3.2.2 Designs for Fatigue Optimization

Figure 3.2.3 High-Quality Fundamental Phase Data

Figure 3.2.4 Characterization of Time- and Environment-Dependent Degradation 

Phenomena

Figure 3.2.5 Data Capture for High-Temperature Alloys

Figure 3.3.1 Relationships between Fundamental Elements of Data

Figure 3.3.2 Higher Accuracy Methods for Modeling Catalysis and Reactions

Figure 3.3.3 Mesoscale Catalyst Modeling and Mesoscale Structure-Activity Relationships

Figure 3.3.4 High Throughput Approaches to Integrated Experimental- and 

Computational-Driven Materials Design

Figure 3.3.5 Integrated Predictive Tools for Heterogeneous Catalytic Reaction 

Mechanisms

Figure 3.4.1 Optimized Joining Methods

Figure 3.4.2 Models to Connect Phase Properties

Figure 3.4.3 Data Repositories for Aluminum, Titanium, and Magnesium

Figure 3.4.4 Large Reduction in Data Costs

Figure 3.4.5 Standardized Definitions for Maturing Levels of Computational Models, 

Experimental Data, and Simulation

Figure 3.4.6 Multi-material Models that Include Texture and Structure
Workshop Report: Building the Materials Innovation Infrastructure: 

Data and Standards

1

1 INTRODUCTION

1.1 THE MATERIALS GENOME INITIATIVE

The Materials Genome Initiative (MGI) is a multi-agency multi-stakeholder effort to develop the 

infrastructure needed to enable the materials science community to design, develop, 

manufacture, and deploy advanced materials at least twice as fast as possible today, at a fraction 

of the cost. This new Materials Innovation Infrastructure (MII) will leverage advances in materials 

modeling, computing, and communications to accelerate advanced materials design and 

deployment in the United States across many fields. More widespread implementation of 

advanced materials will contribute to new products with enhanced functionality, and potentially 

enhance U.S. global competitiveness.

Major advances in theory and modeling have led to a remarkable opportunity for the use of 

computational simulation in predicting the behavior of material systems. However, such 

computational tools are not in widespread use today due to limitations in their capabilities, a lack 

of expertise needed to employ them, and a general lack of confidence in accepting conclusions

that are not empirically based. Similarly, advances in networked communications have led to 

remarkable opportunities for the sharing of technical information, such as materials property 

data. This too has had limited use due to the lack of suitable data repositories, standards, and 

incentives for sharing. 

A recent report emphasizes the growing importance of manipulating, mining, managing, analyzing, 

and sharing scientific data.1 The issues outlined in this report are highly relevant to the MII, which 

will coordinate large amounts of diverse scientific information related to materials.

The MGI is addressing some of these issues by developing a MII that includes: (1) accurate 

models of materials performance validated using experimental data, (2) open-platform 

frameworks to ease the development and interoperation of simulation codes, (3) software that is 

modular and user-friendly with applicability to broad user communities, and (4) data repositories 

built on community standards and outfitted with modern search, retrieval, and analysis tools. 

Achieving these objectives will help leverage existing Federal investments in computational 

capabilities and data management, and provide an integrated approach to materials science and

engineering.

 

1 Atkins, D.; Baker, S.; Dietterich, T.; Feldman, S.; Hey, T.; Lyon, L. National Science Foundation Advisory 

Committee for Cyberinfrastructure Task Force on Data and Visualization Final Report; National Science 

Foundation: March, 2011, http://www.nsf.gov/od/oci/taskforces/.
2 Length-scale Challenges

1.2 WORKSHOP OVERVIEW

The Materials Genome Initiative Workshop – Building the Materials Innovation Infrastructure: Data and 

Standards was held on May 14-15, 2012 in Washington, DC to help define the cross-cutting and 

domain-specific data challenges facing the creation of the MII. The workshop was hosted by the 

National Institute of Standards and Technology (NIST) as part of its broad-based efforts to 

develop new integrated computational, experimental, and data informatics tools. Attendees at 

the workshop included over125 experts from industry, national laboratories, government 

agencies, and academia with interdisciplinary interests ranging from the physical and material 

sciences and engineering to mathematicians and computer scientists.

1.2.1 Workshop Process

Participants were asked to identify challenges, gaps, and opportunities for the development of the 

MII in the following areas:

• Data Representation and Interoperability. This includes an assessment of the types of data that

should be collected and the most efficient digital representation. Also of interest is the 

identification of needed metadata (i.e., additional information that describes properties of the 

data itself and is necessary to make the data usable). Standards may play a particularly useful 

role here. If the community can agree on specific data formats and communication protocols, 

then the retrieval, exchange, and reuse of digital data is greatly enhanced. 

• Data Management. This includes techniques and tools needed for the organization and 

maintenance of digital data repositories. Some of the main issues include incentives for 

making contributions to repositories, methods for access and use that respect intellectual 

property rights, and long-term sustainability of data archives.

• Data Quality. In general, data is of high quality if its origins are known, the methods by which 

it was obtained are fully documented, and its uncertainty has been carefully quantified. Data 

quality can be facilitated if uniform formats for documenting data provenance are available, 

and if reliable techniques and tools exist for uncertainty quantification of results produced 

from both experiment and simulation. 

• Data Usability. Data is usable if users can find what they need, can easily extract it from 

available repositories, and have access to techniques and tools to perform the necessary 

follow-up analyses. 

The workshop generated input based on two separate breakout topics: 1) length scale of the 

phenomena under study, and 2) a set of representative technical application areas (TAAs). 

1.2.1.1 Data Challenges at Length Scales

The design and assessment of materials requires modeling and experimentation at all length and 

time scales. Unfortunately, no single model can feasibly include all such details and their effects.

As a result, computational models typically focus on a particular length scale, i.e., atomic, nano
Workshop Report: Building the Materials Innovation Infrastructure: 

Data and Standards

3

and molecular, micro, and macro. The modeling techniques and data needs for each of these 

scales are potentially quite different. Additional data challenges are created when there is a need 

to use separate models operating on different length scales to obtain a combined result. The

breakout sessions in this area focused on the data and data infrastructure issues relevant to

different length scales, considering data derived from both computational and experimental 

techniques. A key metric for all length scale discussions was how much the time required for 

materials design could be reduced if challenges were addressed. 

1.2.1.2 Data Challenges for Technical Application Areas

New materials are typically developed with a particular application in mind. The ease of 

development depends on whether the particular data and tools needed for that application are 

available and sufficiently capable. Thus it is critical 

to consider data issues in the context of specific 

technical application areas (TAAs). TAA 

breakouts included in the workshop are shown in 

Figure 1.1; these also provided additional context 

for linking the issues identified for length scales 

and associated time scales. 

The rationale for selecting these TAA included: (1) the existence of an established community; 

(2) coverage of a broad range of materials systems and problems; and (3) representation of 

important topics aligned with goals of the participating federal agencies. The TAAs selected are 

only a few of those to be considered within the MGI; they represent a starting point for how to 

integrate application-oriented viewpoints into the materials science and engineering data 

challenges. A key metric for all TAA discussions was how much the time required for materials 

design (specific to the TAA) could be reduced if data challenges were addressed. 

1.3 ORGANIZATION OF THE REPORT

This report summarizes the concepts generated at the workshop and is organized around the 

length scale and technical application area topics described previously. Each length scale chapter 

includes an overview of the topic and a discussion of the major short-term and long-term 

challenges. For each TAA, an overview of the topic and data challenges is provided, as well as 

more detailed descriptions of the higher priority challenges (i.e., related research and 

development (R&D) activities, milestones, and potential benefits).

It should be noted that the ideas presented here are a reflection of the workshop attendees and 

not necessarily the entire community of interest. However, considerable effort was made to 

ensure that participants represented all aspects of the field. Since results for each topic area 

were generated by independent focus groups, the opinions presented for each may differ.

Figure 1.1. Workshop Technical 

Application Areas

• Electrochemical Storage

• High Temperature Alloys

• Catalysis 

• Lightweight Structural Materials 
4 Length-scale Challenges

2 LENGTH SCALE CHALLENGES

The challenges identified for the various length scales considered are described in the following 

sections. For all length scales a voting process was employed to enable categorization of the 

challenges into high, medium, and low priority, and into the timeframes corresponding to the

greatest potential impact. Short term impact was defined as less than five years; long term impact 

as five or more years. Some challenges were noted to have both short- and long-term impacts; 

these are discussed separately in each section. It is acknowledged that all of the challenges 

identified are impediments to the material innovation and design cycle. The categorization 

scheme was employed to provide added perspective on the relative importance and timing for 

impacts if addressed.

2.1 MACRO LENGTH SCALES

The macro length scale is the vantage point where objects, actions, and time can be clearly 

measured and observed by the human eye without significant additional equipment. At this scale, 

the properties and behavior of various material systems are, in a sense, a global average of the 

properties at individual points. Significant amounts of data can be captured for analysis and 

prediction of materials performance and ultimately tied to service life.

2.1.1 Overview of Macro Length Data Challenges 

The macro length scale data challenges that can potentially slow the materials design cycle are

listed in Table 2.1 and further described in the following sections.

2.1.2 Top Short-Term Macro Length Challenges

Unknown metadata requirements: While it is generally agreed that the standard inclusion 

of metadata with primary data would greatly aid in the usability, representation, and 

interoperability of data, the exact metadata required to realize these benefits are not known. 

Additional studies are needed to create a list(s) of necessary metadata fields that would benefit 

all collected data, both experimental and computational. 

Data transfer through the supply chain: In addition to information sharing protocols to 

protect intellectual property, steps need to be taken to ensure that data moving from 

fundamental science to product development be properly handled to avoid real or perceived 

intellectual property constraints. 

Translation to different formats: In data collection systems, on-going configuration changes, 

such as different hardware and/or operating systems, will likely necessitate translation of 
Workshop Report: Building the Materials Innovation Infrastructure: 

Data and Standards

5

information into a format that can be used by receiving systems – either now or in the future. 

Furthermore, basic translation methods between the identified formats would facilitate data 

interoperability and complement any implemented data standardization. Fundamental 

requirements for software packages would also facilitate the accessibility and interoperability of 

new and legacy computing architectures.

Table 2.1 Macro Length Scale Challenges

(t = one vote for potential short-term impact/<5 years)

(l = one vote for potential long-term impact/5 years and beyond)

Data Representation and Interoperability

High 

Priority

• Inadequate understanding of metadata required for primary data tttttttttt (10)

• Ensuring data life (maintaining and archiving) lllllllll(9)

• Identifying/determining data requirements ttttllll(4,4)

• Definition of data or metadata for particular applications with standards for software to 

facilitate linkage ttttttl(6,1)

Medium 

Priority

• Cost of maintaining database updates lllll(5)

• Extending macro models to capture new phenomenon where parameters do not reflect the 

physics (e.g., deformation resistance for thinning in crystal plasticity) ttll(2,2)

• Knowing which entities are working on relevant topics (e.g., Alloy Phase Diagram 

International Commission) tll(1,2)

Lower 

Priority

• Lack of common ontologies or vocabularies (and software systems that can use them) to 

describe concepts, properties, objects, and relations in the material science domain tt (2)

• Generation of grade-specific rather than just generic data (e.g., for plastics)

• Establishing model material systems which represent classes of mechanisms and features

Data Management

High 

Priority

• Protecting intellectual property of data and R&D results ttttttttlllllll(8,7)

• Lack of a national database for materials data; open source and accessible for use by various 

eco-systems ttttttllllllllllll (6,12)

• Transferring data across supply chains (i.e., intellectual property issues) ttttttlll(6,3)

Data Quality

High

Priority • Lack of data quality index that includes the quality of specific properties such as modulus, 

low-cycle fatigue life, and best method specimen finish lllllll(7)

Medium 

Priority

• Performing validation studies on commercial materials ttlll(2,3)

• Generation of data with perspective on future uses tttl(3,1)

• Capturing full multi-axial response of material behavior with all associated conditions (e.g., 

micro structure, boundary conditions, and processing history, finish) ttll(2,2)

• Validating modeling and experimental data and creating protocols for data collection and 

interpretation lll(3)

• Detailing computational or experimental design with calibration against an accepted 

standard to assure data quality ttt(3)

• Useful standards for experimental data (e.g., collection methods, interpretation) ttl(2,1)

Lower 

Priority

• Identifying the key parameters that link one model type (e.g., thermo) to another (e.g., 

mechanical and strain) tt (2)

• Obtaining path dependent information that impacts structure (e.g., chemistry and cooling 

rate)tl(1,1)

• Gaining broad buy-in to minimum or sufficient provenance so that data is acceptable (i.e., a 

standard that “proves” data quality) t (1)
6 Length-scale Challenges

Table 2.1 Macro Length Scale Challenges

(t = one vote for potential short-term impact/<5 years)

(l = one vote for potential long-term impact/5 years and beyond)

• Determining the role of surface characterization and bulk measurements (e.g., deterministic 

vs. probabilistic tools) l(1)

• Generating experimental data (cross method correlation, repeatability)

Data Usability 

High 

Priority

• Standardizing interpretation of export control requirements for materials data and models 

ttttttlllllll(6,7)

• Standardizing formats and metadata requirements for reporting and databasing test data 

tllllllllll(1,10)

• Creating a centralized repository with a standard that researchers can apply

tlllllllll(1,9)

• Translation of data to different formats to interface with different software applications 

tttttt(6)

Medium 

Priority

• Creating tools for merging and analyzing multimodal, 3-D data (e.g., multiple types of 

information, chemical properties, and structures) per pixel tlll(1,4)

• Building automated upload and transfer tools for standardized data exchange formats (e.g., 

Free Data Capture) tttl(3,1)

Lower 

Priority

• Lack of clear intellectual property definitions and definition determination t(1)

• Lack of data tools to support fusion of different types of data, validation of data 

accuracy/consistency, and combining of multi-scale data types

• Attaching metadata to data during collection from the instrument

2.1.3 Top Long-Term Macro Length Challenges

National database: One or more central repositories are needed to warehouse data from all 

entities that generate materials data. These central repositories would be openly accessible and 

contain standardized information uploaded by researchers and others in the field. The standard 

formats and metadata requirements would depend on the sectors served by and contributing to 

the database. These standards would allow any interested party to act as a repository, enabling a 

federation of the database structure. The GenBank at National Institutes of Health is one

example of this data warehouse concept.2 The Materials Project, a collaborative effort between 

the Massachusetts Institute of Technology (MIT) and Lawrence Berkeley National Laboratory

(LBNL), is a data warehouse (albeit at the atomic scale) focused on materials relevant to the 

Materials Genome.3

Ensuring data life: Standards for maintaining and archiving collected data needs to be 

established in conjunction with a national data warehouse. Protocols are needed to ensure that 

data can be retrieved on-demand with high fidelity and immediate utility with all necessary 

translation or conversion methods already in place. 

 

2 Ostell, J. M., Integrated Access to Heterogeneous Data from NCBI. IEEE Engineering in Medicine and 

Biology 1995, 14 (6), 730-736, http://dx.doi.org/10.1109/51.473267. 3 https://www.materialsproject.org
Workshop Report: Building the Materials Innovation Infrastructure: 

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7

Data quality index: Many issues can affect data quality, including data consolidation from 

various systems, external data that is not easily integrated, duplicate data, and various 

measurement methods, processes, and equipment. Data inaccuracies can lead to large 

productivity losses and disappointing project returns. With the large increase in data generation, 

data quality and early detection of unreliable data is extremely valuable. 

2.1.4 Macro Length Challenges with both Short- and Long-Term 

Impacts

Intellectual property of data and R&D results: Intellectual property protection ranks highly 

in both short- and long-term impacts due in part to the large financial implications for product 

and model development. Intellectual property generated by an organization is a collection of 

unique items that provide knowledge and economic benefit to the organization, and is a source 

of competitive advantage of differentiation. When creating the MII, the intellectual property of 

data must be protected not only to safeguard the interests of individual organizations but to 

promote trust among the users. 

Short-term goals could include identifying intellectual property concerns of the community and 

developing protocols for information sharing. Once a framework is in place to address 

intellectual property concerns, maintenance and monitoring of the framework would be 

necessary over the long term to balance pre-competitive data sharing with proprietary or

competitive information.

Standardized export control requirements: Export control regulations are federal 

regulations designed to limit the exchange of selected commodities, services, software, technical 

data, and other information to foreign countries or persons, either in the U.S. or abroad. Items 

of concern involve dual use technologies (i.e., technology which can be used for both peaceful 

and military aims). An example is rocket technology which was developed to carry humans into 

space but also provided the knowledge for development of intercontinental ballistic missiles.

Most industrial countries have export controls on designated dual-use technologies without the 

permission of the government, and penalties for non-compliance can be substantial. The 

application of these regulations when exchanging material data and material models is 

complicated. Transfer restrictions depend on the technology specifics and the destination 

country. Standardized interpretation of these regulations could facilitate long-term data-sharing 

and collaboration related to materials data and models. As regulations change over time it will 

also be important to incorporate updates to the standard interpretation, disseminate periodic 

information to stakeholders, and determine ownership of the standard interpretation over the 

long-term.
8 Length-scale Challenges

2.2 MICRO LENGTH SCALE

The micro length scale focuses on the microstructural features that are relevant to many of the

manufactured specialty materials. Some of the phenomena encompassed at the micro length scale

include diffusion, phase changes and boundaries (e.g., phase diagrams), and phase field modeling. 

Materials microstructure data can be generated from experimental observations or computer 

simulations.

2.2.1 Overview of Micro Length Data Challenges 

The micro length scale data challenges that can potentially slow down the materials design cycle

are listed in Table 2.2 and further described in the following sections.

Table 2.2 Micro Length Scale Challenges

(♦ = one vote for potential short-term impact/< 5 years)

(● = one vote for potential long-term impact/5 years and beyond)

Data Representation and Interoperability

High 

Priority

• Generation of data compatible with commercial codes and usable by non-material

scientists ♦♦●●●●●●●●●●●●● (1,13)

• Choosing an adequate schema (e.g., organization and fields) that is appropriate for 

precompetitive research, evolvable, and flexible enough to handle future modifications

♦♦♦♦♦♦♦♦♦♦♦♦♦♦ (14)

• Lack of key information: full pedigree, test and volumetric data for chemistry, phase, 

orientation, defects at appropriate scale to represent property changes, universal 

identifiers or identification of materials (e.g., important microstructural characteristics 

and defects) ♦♦♦●●●●●● (3,6)

• Developing intuitive application programming interfaces (APIs) that allow users to scale, 

link-up, and integrate different types of data to and from models ♦●●●●●●● (1,7)

Medium 

Priority

• Standardization of data formats (at least within subcategories) to provide consistency for

research done in relative isolation ♦●●●●●(1,5)

• Replacing poorly-defined data with definitions for curation and enabling use of the data 

in some form (e.g., interaction of tools) ♦♦♦●●(3,2)

• Lack of methods for determining similarities of microstructures and standard validation 

and verification tests ♦●●● (1,3)

Lower 

Priority

• Handling sensitive property data (e.g., regulatory) ♦♦♦ (3)

• Developing robust and self-consistent phenomenological theory ♦●● (1,2)

• Lack of an anonymous, proprietary data match-making tool ♦♦ (2)

• Establishing requirements for code that can analyze data generated by unrelated

instruments or simulations ♦ (1)

Data Management

High 

Priority

• High cost and time required to archive and manage data (incentives are lacking)

♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦●● (15,2)

• Lack of data scientists ♦♦♦♦♦♦♦♦♦●●●●● (9,5)

• Lack of means to reference or cite a dataset to maintain “ownership” during data 

sharing ♦♦♦♦♦♦♦♦♦♦●●●● (10,4)

• Lack of sufficient data storage capabilities: ●●●●●●●●●●●● (12)

– Automated data storage protocols that add metadata to facilitate both machine and 

human-centric search and find strategies
Workshop Report: Building the Materials Innovation Infrastructure: 

Data and Standards

9

Table 2.2 Micro Length Scale Challenges

(♦ = one vote for potential short-term impact/< 5 years)

(● = one vote for potential long-term impact/5 years and beyond)

– Federated data storage networks4 that enable data access

– Data storage retrieval and transformation that is compliant with International Traffic 

in Arms Regulations

• Lack of standards, centralized storage and maintenance of experimental and computed 

data, including data storage solutions available to the public ♦♦♦♦♦♦♦♦♦♦♦●●●●●●● 

(11,7)

Medium 

Priority • Unknown formats and quality of structured and unstructured data ♦♦♦● (3,1)

Lower 

Priority

• Decreasing the cost of computing ♦♦♦ (3)

• Comparing data management based on economic security, national security and defense, 

and common global needs ♦● (1,1)

• Rising cost of unused data; determining when to delete data

Data Quality

High 

Priority

• Developing uncertainty quantification tools for modeling and simulation to automatically 

calculate prediction uncertainty, taking into account the origin and accuracy of input data 

and computational methods ●●●●●●●●●●●●●●●●●●● (19)

• Obtaining key information to support quality, including pedigree of data, amount and 

quantity, verification and replication ♦♦♦●●●●●● (3,6)

• Standardizing work-flows to enhance collaboration opportunities ♦♦●●●●● (2,5)

Medium 

Priority • Gaining greater involvement from instrument manufacturers; most original data has 

relevant metadata ♦♦♦♦ (4)

Lower 

Priority • Establishing standards for accessing and improving data quality and preserving legacy data

●● (2)

Data Usability 

High 

Priority

• Lack of tools to easily find existing data; text mining issues arise with legacy data

♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦●●●●● (25,5)

• Lack	of	tools	for	retrieving	data (similarity	metrics),	evaluating	information,	or	assessing	

knowledge	gain	associated	with	datasets, and	lack	of	standards	for	integrating	these	

methods	♦♦♦♦♦♦●●●●●●●●● (6,9)

• Scaling tools to terabyte data sets ♦●●●●●●●● (1,8)

 

4 Federated storage refers to storage resources that are governed by a common management system with 

rules for data storage, management, and migration throughout the storage network. Storage capacity 

would be managed by controllers or appliances controlling multiple arrays.
10 Length-scale Challenges

2.2.2 Top Short-Term Micro Length Challenges

Common data schema: A number of challenges with short term impact relate to choosing 

and developing a common schema (e.g., organization and fields). The schema should be evolvable 

and flexible so it can handle future adaptations. A general schema appropriate for use at the 

precompetitive research level could help promote data sharing during the early stages of 

materials design and development. A significant challenge for data usability is the lack of tools to 

easily locate existing data, especially legacy data where text mining issues often arise. An 

example of useful common schema is the Chemical Markup Language (CML) Schema 

(http://www.xml-cml.org/schema/). CML and the Polymer Markup Language extensions for 

material information have been described in a recent article.5 Other relevant markup languages 

include MatML (http://www.matml.org/), ThermoML (http://trc.nist.gov/ThermoML.html), and 

UnitsML (http://unitsml.nist.gov/).

Data archiving: Archiving data poses a challenge for data management because it requires both 

time and financial investments. Companies and organizations with limited funds may not perceive 

archiving as a priority. Research entities in particular may not be able to archive data due to the

time and resources required. Incentives may be needed to help promote and enable more data 

archiving. An example of this in the public sector is the National Science Foundation’s Extreme 

Science and Engineering Discovery Environment.

Data ownership: Many organizations maintain ownership of data to retain competitive or other 

advantages. While this is a reasonable organizational priority, it can significantly restrict the level 

and types of open sharing that will be needed to create the MII. Before sharing can be more 

widely enabled, mechanisms are needed to protect company or organizational data when 

necessary while minimizing restrictive ownership protocols.

2.2.3 Top Long-Term Micro Length Challenges

Data compatibility: Ensuring that data is both compatible with commercial software and 

usable by non-materials scientists is a primary challenge for enabling widespread use of datasets. 

With the creation of a standard set of basic codes, dataset could be made broadly compatible, 

improving accessibility to virtually any individual or organization.

Handling of massive data sets: Datasets are rapidly growing in complexity and scale. The 

future challenge will be to ensure that information access and usability tools can handle much 

larger datasets, including those up to terabyte levels. 

 

5 Adams, N.; Winter, J.; Murray-Rust, P.; Rzepa, H. S., Chemical Markup, XML and the World-Wide Web. 

8. Polymer Markup Language. Journal of Chemical Information and Modeling 2008, 48 (11), 2118-

2128, http://dx.doi.org/10.1021/ci8002123.
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Error and uncertainty quantification: The quantification of error and uncertainty is a 

significant challenge with all data sets. Error quantification is tied to data analysis, particularly at 

similar time, length, and dimensional material complexity scales.

Data storage: A major challenge for data management over the longer term involves the 

development of automated data storage protocols and federated data storage networks. It would 

be beneficial to develop standardized work flow processes as well as data storage protocols that 

automatically add metadata to facilitate both machine and human centric search and find 

strategies. 

2.2.4 Micro Length Challenges with both Short- and Long-Term 

Impacts

Data scientists: Data scientists employ techniques and theories from many fields, including 

math, statistics, data engineering, pattern recognition and learning, advanced computing, 

visualization, uncertainty modeling, data warehousing, and high performance computing with the 

goal of extracting meaning from data and creating data products. The lack of access to skilled 

data scientists is a contributing factor in many of today’s shortcomings in data management and 

exploitation. The shortfall is expected to increase as the amount and complexity of data 

generated continues to grow at an exponential pace. 

Data retrieval and integration tools: Data retrieval and mining methods and associated 

analyses and algorithms (e.g., similarity metrics) are lacking today and are needed to improve data 

usability now and in the future. Tools to evaluate the information or knowledge gain associated 

within datasets (i.e., understanding and measure of data utility) are also limited. Standards are 

lacking for integrating or linking disparate retrieval methods, which will impede the linking of 

datasets from diverse sources. All of these issues will become more critical as the amount of 

data collected increases.

Central data storage: A publically availability centralized storage and maintenance system for 

both experimental and computational data is a priority challenge for both the short and long 

term. Data standards that allow for consistent management of day are a key component. 

However, the cost and time to develop these systems will be significant and as a result they will 

be difficult to develop and maintain without national or international participation.
12 Length-scale Challenges

2.3 NANO AND MOLECULAR LENGTH SCALE

The nano and molecular length scale acts as a bridge between the micro and atomic length scales 

and is increasingly important to materials due to the current push towards nanotechnology and 

nanoscience. The molecular level was included in this length scale in order to encompass 

phenomena largely at the atomic scale (with even more complexity) and growing interest in new 

properties that emerge at the nanoscale.

2.3.1 Overview of Nano and Molecular Length Data Challenges 

The nano and molecular length scale data challenges that can potentially slow the materials 

design cycle are listed in Table 2.3 and described below.

Table 2.3 Nano and Molecular Length Scale Challenges

(♦ = one vote for potential short-term impact/<5 years)

(● = one vote for potential long-term impact/5 years and beyond)

Data Representation and Interoperability

High 

Priority

• Creating a taxonomy to systematically map out materials spaces down to the nanoscale 

♦●●●●●●●●●●●●●●● (1,15)

• Developing stringent standards for what is considered "data" ♦♦♦♦●●●●●●●●(4,8)

• Limited ability to move from data to credible information and decision making ♦♦♦●●●● (3,4)

• Informing the community about web standards for data representation ♦♦♦♦♦♦ (6)

Medium 

Priority

• Creating representations of particle distributions in nanocomposites (beyond atomic force 

microscopy pictures) ♦♦♦♦ (4)

• Transitioning between models (lack of a single model representation) ♦●●● (1,3)

• Lack of pre-competitive data for model development ♦♦♦♦ (4)

• Consistently using proper definitions (e.g., “a property that emerges under certain conditions 

relative to a material that exists at those conditions") ♦♦♦ (3)

• Overcoming data model dependency at the nanoscale when models are not currently known 

(i.e., complexity at a scale larger than nanoscale may lead to emergent behavior) ♦●● (1,2)

Lower 

Priority

• Lack of the following: ♦ (1)

– Potential functions to model all species of interest through bond breakage 

– Standards for reporting validation of potential functions 

– True multi-scale simulation capability 

– Nanoscale data for validation

• Continuously improving timescale data 

• Relating the product to the materials property data

Data Management

High 

Priority

• Lack of incentives to disseminate data ♦♦♦♦♦♦♦♦♦♦● (10,1)

• Constructing a metadata interface between disparate databases ♦♦♦♦♦♦♦♦♦♦ (10)

• Establishing a flexible framework to allow new forms of data and storage ♦♦♦♦♦♦♦♦●● (8,2)

Medium 

Priority

• Accessing proprietary experimental data and using it with other databases and simulation data 

♦●●●● (1,4)

• Ensuring data flexibility in the MII ●●●● (4)

Data Quality

High 

Priority

• Establishing/improving the provenance of experimental data and models ♦♦●●●●●●●● (2,8)

• Avoiding a strict focus on specific accuracy targets in simulations ♦●●●●●●● (1,7)

• Systematically comparing and validating density function theories against experiments and 

higher-order theories ●●●●●● (6)
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Table 2.3 Nano and Molecular Length Scale Challenges

(♦ = one vote for potential short-term impact/<5 years)

(● = one vote for potential long-term impact/5 years and beyond)

Medium 

Priority

• Life prediction of materials without physical testing ♦♦●●● (2,3)

• Lack of methods to ensure proper attribution and create a culture of sharing ♦♦♦♦♦ (4)

• Providing a sufficient pedigree of data to support product warranties ♦●●●●(1,4)

• Properly defining standard reference data and archival data ♦♦●●(2,2)

Lower 

Priority

• Evaluating the quality of data ♦(1)

• Providing access to experimental data in open literature with sufficient logic or condition 

information ♦ (1)

• Determining the qualitative and quantitative accuracy of error bars

Data Usability 

High 

Priority

• Representing real material data in process-structure and structure-property relationships

♦♦♦♦♦♦♦♦●(8,1)

• Creating a system to balance the needs of the individual and community while sharing and 

distributing data ●●●●●●●● (8)

Medium 

Priority

• Limited network of data and models (with uncertainties) to make multi-physics predictions;

lack of tools for sensitivity analysis and decision making ♦♦●●(2,2)

• Determining if data is reproducible ♦♦♦● (3,1)

• Determining appropriate amount of metadata and meaning behind data collected ♦●● (1,2)

Lower 

Priority

• Linking data with reality via model verification and/or repeatable control of materials 

manufacturing equipment ♦● (1,1)

• Developing data ontology

• Complementing data with tools to build virtual models

2.3.2 Top Short-Term Nano and Molecular Length Challenges

Data dissemination and sharing: Sharing data poses a challenge as organizations are 

concerned about protection of scientific work and intellectual property. More widespread, open 

data dissemination will be necessary to build a successful MII, but incentives are currently lacking 

to encourage this higher level of data sharing. Mechanisms or incentives to encourage data 

dissemination could potentially be provided via authorship credit, acknowledgements, a reward 

system, or other incentives. An example of this concept is the World Wide Web Consortium, a 

community-driven site that serves as a comprehensive and authoritative source for web 

developer documentation. A recent study illustrates what motivates scientists to share data.6

Metadata interfaces between databases: Another significant challenge for data management 

is developing and constructing a standard metadata interface between disparate databases. 

Databases vary in form, amount, type, quality, and properties, and methods are needed to access

the metadata when utilizing or linking different databases. This would allow data to be easily 

shared even through different software systems.

 

6 Tenopir, C.; Allard, S.; Douglass, K.; Aydinoglu, A. U.; Wu, L.; Read, E.; Manoff, M.; Frame, M., Data 

Sharing by Scientists: Practices and Perceptions. PLoS ONE 2011, 6 (6), 

e21101, http://dx.doi.org/10.1371/journal.pone.0021101.
14 Length-scale Challenges

Representing real materials data in process-structure/structure-property 

relationships: Process-structure and structure-property relationships are essential to the 

understanding and development of materials. Today challenges exist in representing real nano 

and molecular materials data so that it can be effectively used in process-structure and structureproperty relationships. While application of real materials data to these relationships is a 

significant challenge, if achieved it would provide greater capability to identify the most suitable

materials for structural applications while avoiding overdesign.

Flexible data frameworks: Establishing a flexible data management framework poses a 

significant challenge. An extensible framework must allow for the incorporation of new forms of 

data and storage for emerging models while accommodating current data and storage protocols. 

A flexible framework must also be able to integrate new data into existing and emerging models

to facilitate useful data sharing.

2.3.3 Top Long-Term Nano and Molecular Length Challenges

Taxonomy for mapping materials spaces: A significant challenge with long-term impact for 

data representation is the lack of taxonomy or coding scheme to systematically map out 

materials spaces down to the nanoscale. A mapping of materials spaces to corresponding 

applications (including situation-specific data) would provide valuable information that could be 

shared with the materials community to facilitate and speed materials design.

Stringent standards for data: The current lack of standards for what qualifies as ‘data’ makes

it difficult to integrate and share different types of data between different applications and 

disciplines. More stringent data definition standards are needed to enable data identification and 

facilitate sharing and transfer and allow researchers to better utilize external sources of data in 

their models. 

Provenance of experimental data and models: Establishing the provenance of experimental 

data and models continues to be a challenge. Provenance establishes the history, transformation, 

and ownership of the data, model, or other object. As data is increasingly shared, users will need 

to be assured that experimental data or models from various sources is of high quality by 

understanding its origin and measurement conditions. Provenance is also a key element in using

data to provide credible analysis and information that effectively supports decision-making.

Balancing needs of individuals/communities to share/distribute data: A system is 

currently lacking that can balance the needs of individuals and the larger community when sharing 

and distributing data. The need of researchers and scientists to “own” certain results and data 

should be balanced with the broader interests of the community. In this context, scientists would 

be encouraged to view themselves as part of a collective network working within the larger 

community. 
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Avoiding strict focus on specific accuracy targets in simulation: The degree of accuracy 

required for simulation is situational (i.e., for some applications 80% "right" might be 

sufficient). A total focus on accuracy can lead to a lack of progress, as the simulation/model can 

always be improved. Perfect accuracy is impossible, so the target accuracy should be application 

specific. 

2.3.4 Nano and Molecular Length Challenges with both Short- and 

Long-Term Impacts

Of the challenges identified in this venue, most were selected as having either a strongly shortterm or long-term impact. Some less pressing challenges, such as those below, could have 

comparable short- and long-term components. 

• Determining the appropriate amount of metadata or meaning of the data collected 

• Properly defining standard reference data and archival data

• Predicting the life of materials without physical testing

• Limited ability to move from data (with qualifying data provenance) to information and 

decision making 

 

2.4 ATOMIC LENGTH SCALE

The atomic length scale encompasses several modeling techniques that involve direct simulation 

of atoms, as well as experimental techniques that probe this length scale. Some of the methods 

that the atomic length scale encompasses include Density Functional Theory (DFT) and Quantum

Monte Carlo (QMC) analysis. 

2.4.1 Overview of Atomic Length Data Challenges 

The atomic length scale data challenges that can potentially slow the materials design cycle are 

listed in Table 2.4 and described below.

Table 2.4 Atomic Length Scale Challenges

(♦ = one vote for potential short-term impact/< 5 years)

(● = one vote for potential long-term impact/5 years and beyond)

Data Representation and Interoperability

High 

Priority

• Lack of standards for experimental data representation, including format, experimental 

conditions, level of information, and analysis tools; large number of formats are used for 

reporting data ♦♦♦♦♦♦♦●●●●●●●●●●● (7,11)

• Dealing with a variety of rapidly evolving methodologies and codes, shifting of best practices,

and changes in default settings over time ●●●●●(5)

Medium 

Priority

• Lack of taxonomies (bottom-up or top-down) resulting in limited ontologies and protocols 

for database structure and property definitions (e.g., common metadata definitions) ♦♦●●

(2,2)

• Improving the use of large amounts of simulation data, particularly for soft matter (liquids, 

colloids, polymers, foams, gels, etc.) ♦♦● (2,1)
16 Length-scale Challenges

Table 2.4 Atomic Length Scale Challenges

(♦ = one vote for potential short-term impact/< 5 years)

(● = one vote for potential long-term impact/5 years and beyond)

Lower 

Priority

• Complete disclosure of equilibration procedures prior to production runs of the simulation 

♦♦(2)

• Lack of methods to encode (and form queries) for complex molecular structures ♦(1)

• Developing materials relevant to working conditions; experimental testing and theoretical 

modeling often use conditions that are not sufficiently realistic ♦(1)

• Limited publicly available data – interoperable databases are needed to enable data mining to 

fill gaps

Data Management

High 

Priority

• Inadequate allocation of resources for data management beyond the immediate scope of the 

current project ♦♦♦♦♦♦●●●●●● (6,7)

• Lack of local and global data storage, storage tools, and data collection solutions (i.e., flexible 

access, permissions, scalable infrastructure) ♦♦♦♦♦♦(6)

Medium 

Priority

• Lack of tools enabling online data storage online ♦♦♦♦♦●●(5,2)

• Inadequate database longevity and maintenance ♦♦♦♦(4)

• Limitations due to the lack of computer science and information technology expertise among

materials scientists ♦♦●● (2,2)

Data Quality

High 

Priority

• Lack of defined validation error bars for computational data ♦♦♦♦♦♦♦♦♦●●●●●● (9,6)

• Lack of experiment round-robin tests designed to specifically validate computed properties 

♦♦♦♦♦●●●●(5,4)

• Inability to adequately compare experimental and computational data (i.e., error analysis, 

validation) ♦♦♦♦♦♦♦(7)

• Insufficient incentives for data sharing ♦●●●●●● (1,6)

• Inability to audit calculations, results, and conclusions so they can be reproduced and 

understood ♦♦♦♦♦(5)

• Limited integration of data generation and analysis workflow with storage of results ♦♦●●●

(2,3)

Medium 

Priority

• Limited means to measure reliability of results and data, including error bars, random test 

and audit of data, and understanding of methods ♦♦♦(3)

• Unacceptable propagation of errors and uncertainty quantification using higher-order 

approximation methods; systematic uncertainty quantification for large datasets ♦●●(1,2)

Lower 

Priority

• Lack of agreed-upon experimental datasets to use as standards to validate theoretical 

methods ●●(2)

• Lack of benchmarks to support data quality:

– Poorly defined benchmark problems for quantitatively validating computations at 

extreme points of the design space ♦♦ (2)

– Insufficient experimental benchmarks for testing energy accuracy of computations for 

surface species and nanoparticles between 1 and 6nanometers in diameter ●(1)

– Absence of relevant benchmarks when new methods are introduced ♦(1)

• Lack of trust in data ●(1)

• Inadequate method development for accurate description of materials and chemical activities 

or properties ●(1)

• Lack of methods to improve the energy accuracy of fast methods such as DFT ●(1) Lack of 

methods to systematically cancel known systematic errors in data (e.g., electronic structure) 

to make it more useful ●(1)
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Table 2.4 Atomic Length Scale Challenges

(♦ = one vote for potential short-term impact/< 5 years)

(● = one vote for potential long-term impact/5 years and beyond)

Data Usability 

High 

Priority

• Lack of a single searchable database providing properties linked to composition, 

microstructure, and scale(s) of the experiment of computation ●●●●●●●●●(9)

• Developing open source interfaces to support many data standards; lack of common interface 

for searching across databases ♦♦♦♦♦♦♦♦♦♦(10)

• Lack of complete workflow documentation, including all information necessary for 

reproducing calculations and experiments, for all properties and codes (i.e., providing inputs, 

parameters) ●●●●●●●●●(9)

Medium

Priority • Lacking data quality evaluation “scoring” by later users and scoring accountability (note the 

name of the scorer) ♦♦♦(3)

Lower

Priority

• Limited data storage and technologies that integrate with data analysis for more scalable data 

mining ●●(2)

• Inability to use existing popular, well-developed, mass-scale data solutions (e.g., Flickr, 

Google) ●●(2)

• Tools for high-throughput experimental measurement of energy stability of nanomaterials, 

complex materials (bulk), and surface species ♦●(1,1)

• Information tools available at the database site to prevent data transfer issues ●(1)

• Lack of accurate materials imaging tools (e.g., measurement and visualization of concentration 

gradients and flow) ♦(1)

• Maintaining compatibility of software (platform and software evolution) ●(1)

2.4.2 Top Short-Term Atomic Length Challenges

Computational data validation: Research in general would benefit from error analyses and 

improved data validation efforts. Data quality is hampered by a lack of reportable, defined

uncertainty metrics to enable more rigorous validation of computational data. Uncertainty 

metrics would facilitate more direct assessment of the accuracy and precision of experimental 

and computational data. In addition to these metrics, methods for measuring the reliability of 

results and data are generally limited, including random test and audit of data and understanding

of the methods used to produce data. Benchmarks are also lacking to validate computational data

particularly at the extreme points of the design space.

Inadequately detailed research papers in some cases have highlighted the need for detailed 

validation and verification of results. In addition, metadata could be useful for validation if it could 

be attached to electronic structure data to reflect technical failure or success of a particular 

method. 

Comparability of data: Another challenge impacting data quality is the inability to adequately 

compare experimental and computational data. It is important to understand the limits of using 

computational and experimental data and the potential pitfalls of treating them in a similar way. 

The lack of complete workflow documentation and the high cost of data access compound the 

problem and highlight the need for publicly available, transparent data. 
18 Length-scale Challenges

Database interfaces: Database interfaces remain a challenge for data retrieval and usability. 

Web interfaces that are often used to enable databases can be perceived as an impediment to

research. There is growing interest in allowing APIs to access databases in an automated manner

rather than via web interfaces. The “pymatgen” (http://pypi.python.org/pypi/pymatgen) Python 

library, for example, programmatically accesses the MIT/LBNL Materials Project data

(https://www.materialsproject.org).

It is also difficult to search across multiple large datasets without a means to effectively integrate 

databases. The development and broader use of open source interfaces that support a range of 

data standards will be important to overcoming data retrieval and usability concerns. 

2.4.3 Top Long-Term Atomic Length Challenges

Data compatibility and usability: The lack of standards for representing experimental data 

poses a significant challenge. Data is often produced in proprietary formats, which can render 

further analysis by the community at large difficult or impossible. There are also large numbers of 

formats for reporting data (i.e., input files used for different software packages). These challenges

demonstrate the need for improved methods of data representation that provide information on 

the relevance of the data for a particular application. Information sharing efforts and the 

exchange and uploading of data would benefit significantly from data standardization.

Disparate databases and datasets: One significant challenge is the absence of a single 

searchable database that provides properties linked to composition, microstructure, and scale of 

the experiment or computation. The lack of integration of multiple datasets and property sets in 

a single search environment make searches for specific structures particularly difficult. Tools are 

needed to automatically extract data from the expanding materials science literature; this will 

require the ability to search for useful data in large datasets (i.e., database interfaces).

Workflow documentation and automated data retrieval: A scarcity of complete 

workflow documentation creates a considerable challenge for data retrieval and usability by 

research entities. Failure to disclose necessary inputs, parameters, and other critical information

behind the data or model makes it difficult to reproduce or improve upon the work of other 

researchers. 

Locating relevant data in the open literature is also becoming increasingly difficult as the amount 

of computational reporting continues to rapidly grow. Finding data can be so costly and timeconsuming that it is not feasible given the resources available for the research. An automated 

method for extracting data, hypotheses, and other key information from the literature would 

help to address this growing challenge.
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2.4.4 Atomic Length Challenges with both Short- and Long-Term 

Impacts

Lack of standardization: One of the challenges identified as having both short- and long-term

impact potential is the lack of standardized formats to describe experimental conditions, analysis 

tools, and other practices that would enable comparison of existing and new research in a 

meaningful way. This challenge highlights the need for a standard format or translators to 

standardize experimental and computational data both now and in the future. 

Data validation and error bars: This challenge can have both short- and long-term impacts

on data quality. Evaluating the accuracy and precision of experimental and computational data is 

difficult given the lack of uniform standards and procedures for reporting the margins of error. 

As noted, this impacts the ability to assess the accuracy and precision of the data and to compare 

datasets from similar experiments. This is an on-going challenge that will require sustained

attention to ensure information fidelity.

Data management resources: Allocation of resources for data management beyond current 

project scope is often inadequate and impacts the longevity and utility of data, both in the short 

and long term. In general, the tendency for data to be filed and forgotten limits the use of 

valuable data from past research. Both the lack of standardized formats for storing large 

simulation datasets and the absence of an accessible repository for storing computational data 

demonstrate the need for proactive allocation of resources to manage data so that it can remain 

useful for the long term.

Linked data is an approach that could be relevant for efficient data management. Linked data 

provides a publishing environment where not just documents but data are accessible on the 

Web, extending the Web into a global data space with open standards (e.g., Web of Data). A 

number of resources are available described the concept of linked data, which is gaining interest 

in the scientific community. 7,8

 

7 Heath, T.; Bizer, C., Linked Data: Evolving the Web into a Global Data Space. 1st ed.; Morgan & Claypool: 

2011; Vol. 1,http://dx.doi.org/10.2200/S00334ED1V01Y201102WBE001. 8 Berners-Lee, T., Tim Berners-Lee on the next Web. In TED Talks, TED Conferences, LLC: 

2009, http://www.ted.com/talks/tim_berners_lee_on_the_next_web.html.

Workshop Report: Building the Materials Innovation Infrastructure: 

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3 TECHNICAL APPLICATION AREAS 

3.1 ELECTROCHEMICAL STORAGE

Electrochemical energy storage is an application area of growing interest as the adoption of 

alternative sources of energy increases. Sources such as solar and wind are intermittent, and 

storage capabilities are needed to enable energy capture and on-demand use. Developing 

electrochemical storage is also important to alternative transportation technologies such as

hybrid and electric vehicles. Today’s technologies for energy storage are inadequate to support 

the innovative, next generation grid and transportation capabilities envisioned for the future. 

New materials have the potential to increase the efficiency of devices for energy harvesting, 

storage, and conversion and create viable solutions for storing alternative energy.

3.1.1 Overview of Data Challenges for Electrochemical Storage

The data challenges related to materials design for electrochemical storage are listed in Table 3.1 

and further described in the following sections.

Table 3.1 Electrochemical Storage Challenges

(● = one vote)

Frameworks, Methodologies, and Tools

High 

Priority • Developing and standardizing a simulation and analysis toolbox ●●●●● (5)

• Creating a central data repository and software structure ●●●● (4)

Medium 

Priority

• Building a database with proper indices for searching for solid state materials and 

properties (e.g., similar to the American Chemical Society’s SciFinder research discovery 

tool for organic compounds) ●● (2)

Lower 

Priority

• Building open source development environments for the creation of digital frameworks 

(with data consistency checking and model linking) that are reusable, robust, recomposable, and easily used and accessed by non-materials scientists and 

electrochemists (e.g., the North Carolina State framework); the environment would

have the ability to rapidly create workflows for non-experts ● (1)

• Designing experimental tests for materials benchmarking ●(1)

• Establishing an architecture of databases and systems that can drive layers of information 

and data by emulating computer science methods ● (1)

• Developing a common nomenclature and vocabulary to communicate chemical reaction 

data across disciplines (e.g., fuels cells to batteries) ● (1)

• Extending the MGI framework to enable portability, scalability, and adaptability ●(1)

• Developing methods to analyze and integrate data from experiments and models (e.g., an 

open source interface or a set of basic/common formats)

• Developing a concrete use case for an energy storage problem to demonstrate the 

benefits of integrating shared distributed data and metadata
22 Technical Application Areas

Table 3.1 Electrochemical Storage Challenges

(● = one vote)

Data Capture and Evaluation

High 

Priority • Defining and generating data on materials and systems (safety, lifetime, reliability, and 

cost) ●●●●● (5)

Medium 

Priority

• Evaluating correct structure property data ●●● (3)

• Defining the top twenty queries that the MGI should address ●●● (3)

• Capturing data from unpublished failures ●●● (3)

• Establishing ongoing efforts to digitize existing phase diagrams from old databases and 

publications ●●● (3)

• Researching fundamental data including thermodynamics data and reaction rates (e.g., 

dissolution rate or the transport properties specific to energy storage) ●● (2)

• Creating a system where suppliers provide materials properties with products ●● (2)

Lower 

Priority

• Identifying the most costly and time-consuming priorities in the field of electrochemical 

storage ● (1)

• Gathering all known experimental data and defining figures of merit

• Investigating commonalities across various types of databases 

Cross-cutting

High 

Priority • Communication and interaction of researchers at different stages of development (e.g., 

atomistic to end-of-line) to focus research on relevant materials properties ●●●●●(5)

Medium 

Priority

• Generating relevant, high-impact data using a distinguishing methodology (e.g., 

electrolyte transport properties) ●●(2)

• Researching the flow of data and models as a means of establishing provenance ●● (2)

• Investigating data correlations across applications and end uses (e.g., bad data can yield 

good data science) ●● (2)

Lower 

Priority

• Dividing experimental data modeling needs by technology segment (i.e., grid, 

transportation, or end life) ●(1)

• Enhancing communication and information dissemination across disciplines by creating a 

new material ontology

3.1.2 Top Challenges for Electrochemical Storage

Based on the results of the voting process as shown in Table 3.1, a set of the top data challenges 

impacting the design of new materials for electrochemical storage were identified. If addressed 

these challenges have the potential to speed up the materials design cycle. Top challenges are 

summarized below and described in more detail in Figures 3.1.1 through 3.1.4.

Define and Generate Data on Materials and Systems: Data on both materials and the 

systems into which these materials are inserted must be defined and generated to incorporate 

considerations of safety, longevity, reliability, and cost into design. This will require identification 

of key metrics and targets for these essential parameters. Models for life prediction that are 

enabled by appropriate information and allow researchers to access the data should be 

developed. Lab scale-up to metric tons (at tolerable impurity levels) and full cell and systems 

modeling are needed as well. This would decrease the time for actual testing of real batteries 

from years to weeks or days via modeling. Other benefits include identification of key materials 
Workshop Report: Building the Materials Innovation Infrastructure: 

Data and Standards

23

for modeling, being able to determine the feasibility of scaling up material to production, and 

knowing how a cell will act in a completed battery pack. Figure 3.1.1 provides additional details 

about this challenge.

Standardization of a Simulation and Analysis Toolbox: One of the main data challenges 

for electrochemical storage is the development and standardization of a simulation and analysis 

toolbox via nomenclature standards. Specific tasks include: (1) building community standards for 

experimental and computational analysis for batteries similar to ASTM (American Society for 

Testing and Materials) International standards; (2) building a system of metadata tags; and (3) 

creating a standards methods database that can be revised as needed over time. With a

simulation and analysis toolbox researchers will spend less time reproducing others’ results, 

standards will become R&D proxies for pre-competitive collaboration, and overall materials 

genome discussions will become more systematic. Figure 3.1.2 provides additional details about 

this challenge.

Central Repository and Software Infrastructure: The creation of a central data repository

and software infrastructure incorporating information related to electrochemical storage 

materials could provide numerous benefits, but remains a challenge for electrochemical storage. 

Such a repository would make collaboration and data sharing much easier. A leader or manager

for the central data repository must be identified along with an institution where it can reside. A

business model should be developed to demonstrate how the repository could be sustained, 

including incentives to maintain the infrastructure. Champions (respected entities/individuals 

within the community) of the repository would help to ensure continued support. Successful 

creation of a staffed central repository and software infrastructure could potentially allow crossdisciplinary access to data, enable users to operate analysis tools using shared data, and tie 

together data from different sources. Figure 3.1.3 provides additional details about this challenge.

Communication across Different Stages of Development: To better focus research on 

relevant materials properties, extensive communication must be established between researchers 

working at different stages of development, from atomistic to deployment to in-service. Activities 

include events that bring together academia and industry to identify needs, identification of 

figures of merit for calculated properties, R&D to evolve figures of merits and to inform 

researchers of what data to collect, and the development of electrochemical materials 

descriptors (rather than “properties”). Communication across development stages will lead to a

better understanding of how and why materials perform. Predictive models and the time-tomarket of materials discoveries will also improve with more open lines of communications. 

Figure 3.1.4 provides additional details about this challenge.
24 Technical Application Areas

Figure 3.1.1 Define and Generate Data on Materials and 

Systems

There is a current need to define and generate data on both materials and systems to incorporate considerations of 

safety, longevity, reliability, and cost into design. Key performance targets must be identified to overcome this 

challenge.

Short-term Win

Accelerated discovery and commercialization of new 

materials and systems

Long-term Advance

Commercialization of new materials via cycle life models 

and better scale-up methods

Short-term	Ac7vi7es	

•	IdenGfy	key	performance	targets	such	as	safety,	life,	cost,	

toxicity,	and	reliability	

•	Develop	predicGve	models	for	cycle	life	(with	appropriate	data	

and	data	access	for	researchers)	

•	Perform	scale-up	from	lab-scale	to	metric	tons	including	

tolerances		for	impurity	levels	and	cost	

•	Model	full	cell	and	systems	

Long-term	Ac7vi7es	

•	Verify	experimental	vs.	modeling	cycle	life	

•	Research	experimental	data	for	modeling	inputs	

•	Develop	fundamental	mechanisms	of	failure		

Short-term	Milestones	

•	Determine	impurity	levels	(in	parts	per	million	or	parts	per	

billion)	

•	Develop	predicGve	tools	for	measuring	cycle	life	

•	Develop	integrated	modeling	from	baOery	to	full	system	

Long-term	Milestones	

•	Establish	definiGons	of	failure	mechanisms		

Short-term	Outcomes		

•TAA		Benefits

•	Common	plaSorm	for	all	baOery	models	and	systems	

•ContribuGon	to	MII

•	MulG-scale	modeling	capability	

•	CollaboraGon	across	disciplines	(e.g.,	chemistry,	physics,	

engineering,	and	others)	

Long-term	Outcomes	

•TAA		Benefits

•	Common	plaSorm	for	all	baOery	models	and	systems	

•ContribuGon	to	MII

•	Valuable	data		to	support	future	materials		work	
Workshop Report: Building the Materials Innovation Infrastructure: 

Data and Standards

25

Figure 3.1.2 Standardization of a Simulation and Analysis 

Toolbox

A simulation and analysis toolbox and nomenclature standards would be beneficial to designing materials for 

electrochemical storage. These will reduce the time needed to reproduce work and provide a more systematic 

approach to research. MII will benefit ands community confidence in data will increase.

Short-term Win

Less time spent reproducing results

Long-term Advance

Acceleration of electrochemical storage field through data 

reuse

Short-term	Ac7vi7es	

•	Develop	a	set	of	standard	experimental	techniques	as	a	

guideline	for	invesGgaGng	new	materials	for	baOery	components	

and	proxies	

•	Develop	standard	techniques	for	computaGonal	research	

including	code,	opGmizaGon	paths,	configuraGonal	choices,	and	

other	simulaGon	requirements	

Long-term	Ac7vi7es	

•	Develop	a	community	website	to	house	and	document		common	

standards	and	deliver	best	pracGces				

Short-term	Milestones	

•	DraV	toolbox	standard	compliant	with	standards	organizaGons	

and	material	science	baOery/energy	storage	community	

•	Establish	micro-formats	to	enable	search	of	standards	

•	Complete	a	series	of	workshops	to	evolve	and	extend	the	draV	

standard	

Long-term	Milestones	

•	Track	rate	of	growth	and	use	of	the	standards	in	materials	

science	

Short-term	Outcomes		

•TAA		Benefits

•	Community	adopGon	of	standard	nomenclature	in	papers	

•ContribuGon	to	MII

•	Decreased	Gme	required	for	researchers	to	reproduce	

experiments	and	simulaGons	from	other	researchers		

Long-term	Outcomes	

•TAA		Benefits

•	Useful	legacy	data	for	future	materials	design	

•ContribuGon	to	MII

•	Increased	confidence	of	the	research	community	in	reusing	

data	and	conclusions	as	a	result	of	an	established	toolbox	

standard	
26 Technical Application Areas

Figure 3.1.3 Central Repository and Software 

Infrastructure

A central data repository needs to be created and will require leadership, selection of a facility, development of 

software infrastructure, and a plan for sustained maintenance and growth over time. 

Short-term Win

Connecting data at length scales

Long-term Advance

Acceleration of the design process and improved data 

validation

Short-term	Ac7vi7es	

•	IdenGfy	respected	“champions”	to	ensure	sustained	

support	

•	Develop	a	funding/business	model	

•	Choose	an	insGtuGon	to	house	the	repository	

•	Build	an	incenGve	infrastructure	

Long-term	Ac7vi7es	

•	Facilitate	the	development	of	beOer	baOeries	

•	Incorporate	automaGc	links	to	publicaGons	

•	Change	the	materials	science	and	engineering	culture	for	

how	data	is	shared	and	stored	

•	Improve	communicaGon	across	exisGng	boundaries		

Short-term	Milestones	

•	Establish	a	group	of	community	members	authorized	to	

upload	data	

•	Form	a	governance	board	

•	AOract	more	than	10,000	acGve	users	and/or	contributors	

Long-term	Milestones	

•	Gain	acGve	parGcipaGon	from	the	baOery	community	

•	Obtain	long-term	commitment	from	an	insGtuGon	

Short-term	Outcomes		

•TAA		Benefits

•	Access	to	data	across	community	disciplines	

•	Ability	to	operate	specialized	analysis	tools	on	data	

provided	by	others	

•	AssociaGon	of	data	from	different	sources	

•ContribuGon	to	MII

•	IntegraGon	of	new	tool	s	by	improving	and	evolving	the	

interface	to	data	

Long-term	Outcomes	

•TAA		Benefits

•	Sustained	source	of	usable	legacy	data	

•ContribuGon	to	MII

•	IntegraGon	of	new	tools	by	improving	and	evolving	the	

interface	to	data		

•	ReducGon	of	the	design	process		length	for	new	

materials	
Workshop Report: Building the Materials Innovation Infrastructure: 

Data and Standards

27

Figure 3.1.4 Communication across Different Stages of

Development

Communication needs to be established between researchers working at different stages of development (e.g.,

atomistic to deployment to in-service) to focus on relevant materials properties. This will increase understanding of

materials, improve models, and shorten the time to market for new materials. 

Short-term Win

Identification of key priorities, parameters, and issues of 

greatest impact to industry

Long-term Advance

More effective navigation of the materials genome across 

domains

Short-term	Ac7vi7es	

•	Conduct	workshops	between	academia	and	industry	to	idenGfy	

pre-compeGGve	needs	

•	IdenGfy	figures	of	merit	for	calculated	properGes	

•	Perform	R&D	to	evolve	figures	of	merit	and	inform	researchers	

of	data	collecGon	needs	

Long-term	Ac7vi7es	

•	Develop	electrochemical	materials	descriptors	rather	than	

“properGes"	

•	Advance	data	mining	techniques	

•	Develop	new	analyGcs	for	navigaGng	materials	applicaGons		

Short-term	Milestones	

•	Develop	a	clear	roadmap	of	energy	storage	materials	properGes	

goals	with	shortcuts	

•	Collect	and	disseminate	industry-academia	findings	with	

shortcuts	

Long-term	Milestones	

•	Accomplish	a	'Fundamental	Engineering	Problem	'	on	atomisGcto-design	modeling	chain	of	analysis	

•	Publish	descriptor	database	and	data	analyGcs	

Short-term	Outcomes		

•TAA		Benefits

•	BeOer	understanding	of	why	and	how	materials	perform	

•	BeOer	predicGvity	of	models	

•	Improved	Gme	to	market	for	designed	materials.

•ContribuGon	to	MII

•	Broad	perspecGves	on	materials	data	needs	and	development	

Long-term	Outcomes	

•TAA		Benefits

•	Shortened		Gme		to	design	new		classes	of	electrochemical	

materials		

•	Expanded	design	limits	of	a	parGcular	material	

•ContribuGon	to	MII

•	CreaGon	of	“technology-agnosGc”	descriptor	database	

•	FormaGon	of	an	open,	shared	toolkit	for	data	analyGcs	allowing	

rapid	validaGon	of	data	and	models		
28 Technical Application Areas

3.2 HIGH-TEMPERATURE ALLOYS

High-temperature alloys are used in technologies where the material must withstand extreme 

temperatures, such as in jet engines. These alloys must be able to exhibit creep resistance, high 

strength at extreme temperatures, and high fatigue life, as well as degradation resistance at high 

temperatures.

3.2.1 Overview of Data Challenges for High-Temperature Alloys

The data challenges related to materials design for high temperature alloys are listed in Table 3.2

and further described in the following sections.

Table 3.2 High-Temperature Alloy Challenges

(● = one vote)

Data Quality/Pedigree

Medium 

Priority

• Referencing data for phase-based properties to build multicomponent “databases” (e.g., 

CALPHAD) ●●● (3)

• Understanding statistical confidence but not knowledge ●●● (3)

Lower 

Priority

• Flawed or missing fundamental data for many systems 

• Determine what data history should be required

• Determine the volume of data required to achieve a needed level of certainty 

• Develop data quality assessment metrics that are quantitative

• Reduce amount of time required for the review of independent data sets and consensus 

of results by experts

Data Retrieval/Usability

High 

Priority

• Lack of a definition for high-temperature materials metadata that enables easier data 

retrieval ; lack of defined universal identifiers for materials so data can be located in an 

established database ●●●●●●●●●●●●●●●●(16)

Medium 

Priority

• Visualization of generated information and data ●●●(3)

• Determining the representative volume element for each material or property critical for 

high-temperature systems ●●●(3)

Lower 

Priority

• Determine the grain size and orientation before initial use and at inspection intervals 

(non-destructive evaluation) ●●(2)

• Identify the key microstructural attributes that are most influential to properties ●(1)

• Develop data mining and analysis tools for large data sets

Material Applications

High 

Priority

• Formulation of foundational engineering problems to provide context for data challenges 

●●●●●●●●●●(10)

• Application data needs for materials and quantification of properties ●●●●●●●●●(9)

Lower 

Priority • Linking models between different length scales ●●(2)
Workshop Report: Building the Materials Innovation Infrastructure: 

Data and Standards

29

Table 3.2 High-Temperature Alloy Challenges

(● = one vote)

Data Sharing

Lower 

Priority

• Lack of requirements and a coherent community for obtaining and sharing data 

throughout the high-temperature materials supply chain (e.g., by developing a common 

certified data format, data sharing definitions) ●●(2)

• Lack of incentives for sharing data throughout supply chain that outweigh the immediate 

costs, risks, and inertia ●(1)

• Data encapsulation (for mobility)

Materials Degradation

High 

Priority

• Develop a thorough understanding of the inherently dynamic nature of high-temperature 

materials in service ●●●●●●(6)

• Developing a thorough understanding of the damage and aging phenomena for new hightemperature materials to increase their adoption ●●●●●(5)

Medium 

Priority

• Combining experimental and computational exploration of ternary and higher component 

systems ●●●(3)

• Obtaining high-temperature corrosion data (especially localized corrosion) for multiple 

environments ●●(2)

Lower 

Priority

• Understanding and predicting microstructure and thus mechanical property degradation 

under a complex environment at high temperatures ●(1)

• Enabling science-based processing (e.g., microstructure evolution models, defect 

formation) 

Data Capture

High 

Priority

• Automatically capturing data and metadata when generated, and subsequent transfer and 

sharing with other stages in the work flow ●●●●●●●●●●●●●●(14)

• Long test times and high costs for testing ●●●●●●●●●●●(11)

Medium 

Priority • Determining where/how to store share large amounts of test data (e.g.10 gigabytes of 3-

D structural information) that is generated daily on high-temperature materials ●●●●(4)

Lower 

Priority

• Eliminating duplications of work and pooling data ●(1)

• Developing rapid throughput characterization protocols

• Developing standards for data collection (e.g., test and model methods)

• Characterization and validation of multi-axial data for both deformation and life 

3.2.2 Top Challenges for High-Temperature Alloys

Based on the results of the voting process as shown in Table 3.2, a set of the top data challenges 

impacting the design of high temperature alloys were identified. If addressed these challenges 

have the potential to speed up the materials design cycle. Top challenges are summarized below 

and described in more detail in Figures 3.2.1 through 3.2.5. 

Data Retrieval for High-Temperatures Materials: For high-temperature materials, 

definition of metadata needs to be established so that data searches retrieve all relevant 

information, but does not retrieve extraneous/unnecessary information .Universal identifiers for 

materials should be determined when the database is built so data can be easily located. Pilot 

programs could be used to help demonstrate data retrieval methods. Also, to ensure that all data 
30 Technical Application Areas

and metadata has been captured and can be retrieved, a database schema for high-temperature 

alloys needs to be built, tested, and implemented. Achieving these objectives would facilitate 

validation of models and lead to reduced testing requirements and costs. Figure 3.2.1 provides 

additional details about this challenge.

Designs for Fatigue Optimization: Models and associated data that provide predictive 

capabilities for high-temperature materials properties require further development. This involves 

design for thermal mechanical fatigue (TMF) optimization and component design for location 

specific high-temperature fatigue optimization. Related activities include: (1) development of TMF 

resistant materials; (2) tools to link designer requirements and materials capabilities; and (3) the 

development of materials data and associated material models to accelerate development of 

advanced superalloys for high-temperature applications. The detailed design requirements linking 

model-based alloy design efforts must also be captured. Benefits resulting from overcoming this 

challenge include reduced cost of energy and reduced cycle time for the design of new materials. 

Figure 3.2.2 provides additional details about this challenge

High-quality Fundamental Phase Data: Inexact fundamental multi-component phase data 

prevents the use of higher mechanistic process-structure and structure property models. 

Fundamental phase-based property models should be defined and pure element data with 

uncertainties should be established. Also, an infrastructure should be established for assessments 

and to determine error propagation for high-order systems. The result will be more accurate, 

high-order process-structure and structure-property models as well as the ability to design 

materials faster and more affordably. Figure 3.2.3 provides additional details about this challenge.

Characterization of Time- and Environment-Dependent Degradation Phenomena:

Challenges for the high-temperature degradation of materials include characterizing structural 

and surface degradation, extrapolating short-term tests to life predictions, and the inherent 

complexity and high costs of simulations. Protocols should be utilized for in-service monitoring 

and inspection data as feedback for developing models and assessing components. 

Communication protocols that protect intellectual property and provide relevant data should be 

established to encourage data sharing. Benefits include improved communication between endusers and the supply chain, validated predictive models, and improved life assessments. Figure 

3.2.4 provides additional details about this challenge.

Data Capture for High-Temperature Alloys: Current methods of materials 

characterization are expensive and time consuming; such issues are exacerbated when 

characterizing extreme temperature phenomena. To overcome this challenge a user-friendly, 

fully automated approach is needed for capturing and storing data and metadata. An enabling 

model and mechanism validation would also dramatically reduce physical testing requirements. A 

significant reduction in time to complete a full materials characterization could be achieved if this 

challenge is overcome. Figure 3.2.5 provides additional details about this challenge.
Workshop Report: Building the Materials Innovation Infrastructure: 

Data and Standards

31

Figure 3.2.1 Data Retrieval for High-Temperature Alloys

Data retrieval methods need to be enhanced with improved definitions for high-temperature materials metadata. 

Universal identifiers for materials should be designed in the early stages of database development.

Short-term Win

Accelerated search for high-temperature data

Long-term Advance

Verified model for fast data mining and information 

retrieval 

Short-term	Ac7vi7es	

•	Lay	foundaGon	for	universal	idenGfiers	

•	Build	consistent	descriptors	

•	Build	a	framework	for	data	retrieval	

•	Develop	a	pilot	test	database		

Long-term	Ac7vi7es	

•	Build	database	scheme	

•	Develop	data	mining	tools	

•	Explore	enhanced	visualizaGon		

Short-term	Milestones	

•	IdenGfy	representaGve	team	members	from	both	academia	and	

industry	

•	Develop	a	project	charter	

•	Develop	a	database		using	developed	definiGons	and	idenGfiers	

•	Gather	feedback	from	the	user	community	

Long-term	Milestones	

•	Implement	and	test	database	schema	

•	Implement	and	test	database	data	mining	tools	

•	Develop	interface	tools	for	visualizaGon		

Short-term	Outcomes		

•TAA		Benefits

•	Required	data	or	metadata	is	gathered	

•	Culture	is	changed	through	involvement	of	others	

•ContribuGon	to	MII

•	RealizaGon	of	proof	of	concept	with	the	prototype		

Long-term	Outcomes	

•TAA		Benefits

•	Enabling	of	data	mining		

•	IdenGficaGon	of	new	correlaGons		

•ContribuGon	to	MII

•	Ability	to	build	beOer	models	

•	Reduced	need	for	tesGng	and	reduced	costs	

•	Cut	in	half	development	Gme	
32 Technical Application Areas

Short-term Win

Realization of energy savings from efficient materials 

designs

Long-term Advance

Development of infrastructure to support a future energy

efficiency increases

Figure 3.2.2 Designs for Fatigue Optimization

In the future, materials should be designed with thermal mechanical fatigue (TMF) optimization and specific hightemperature fatigue optimization. Models can aid in the development of fatigue property databases for materials 

used in high temperature applications.

Short-term	Ac7vi7es	

•	Establish	material	models	

•	Collect	data	and	validate	models	

•	Apply	models	to	TMF	and	overall	property	performance	

predicGons	

Long-term	Ac7vi7es	

•	Develop	materials	that	include	usable	models	in	addiGon	to	data	

sheets	

Short-term	Milestones	

•	Create	databases	for	faGgue	data	

•	Develop	models		and	generate	data	

•	Validate	model	or	current	methods	

•	Apply	these	milestones	to	the	predicGon	of	new	alloys	and	

designs		

Long-term	Milestones	

•	Apply	methods	to	other	alloy	systems	and	component	

applicaGons		

Short-term	Outcomes		

•TAA		Benefits

•	Reduced	energy	costs	

•	Reduced	use	of	materials	

•	OpGmized	development	cycle	

•ContribuGon	to	MII

•	CreaGon	of	models	for	future	use	

•	IntegraGon	of	material	models	with	design	

Long-term	Outcomes	

•TAA		Benefits

•	Rapid	design	changes	

•	Reduced	energy	costs	

•	Improvements	to	automoGve	and	aeropace	industries		

•ContribuGon	to	MII

•	Development	of	integrated	computaGonal	science	and	

engineering	skill	sets	within	industry		
Workshop Report: Building the Materials Innovation Infrastructure: 

Data and Standards

33

Figure 3.2.3 High-Quality Fundamental Phase Data

Generation of fundamental multi-component phase data will enable the use of higher mechanistic process structure 

and structure property models. These models will relate material process, structure, and properties to reduce new 

material design time.

Short-term Win

Accurate reference and phase data for pure elements

Long-term Advance

Reliable fundamental data for multi-component phases

Short-term	Ac7vi7es	

•	Begin	defining	models	for	fundamental	phase	properGes		

•	Define	priority	elements	(e.g.,	nickel,	Gtanium,	and	cobalt)	

•	Establish	pure	element	data	with	uncertainGes		

Long-term	Ac7vi7es	

•	Define	error	propagaGon	for	higher-order	system	assessments	

•	Establish	infrastructure	for	user	to	contribute	assessments	and	

associated	data	

•	Develop	incenGviGes		to	encourage		others	to	contribute	

assessments	

•Define	metrics	to	rate		quality	of	an	assessment		

Short-term	Milestones	

•	Define	models	for	fundamental	phase	properGes	

•	Define	priority	elements	(e.g.,	nickel,	Gtanium,	and	cobalt)	

•	Establish	pure	element	data	with	uncertainGes	,	iniGally	with	

priority elements	then	conGnuing	with	all	the	elements	

Long-term	Milestones	

•	Define	and	distribute	error	propagaGon	methods	

•	Delineate	metrics	for	quality	assessment	

•	Develop	infrastructure	for	assessment	development	

•	Populate	the	assessment	development	infrastructure		

Short-term	Outcomes		

•TAA		Benefits

•	Improved	accuracy	of	higher-level	models	

•	Improved	higher-order	assessments	

•ContribuGon	to	MII

•	More	accurate	pure	element	data,	which	is	important	for	the	

enGre	MII	

•	Defined	fundamental	phase-based	property	models	resulGng	in	

improved	efficiency	

Long-term	Outcomes	

•TAA		Benefits:	

•	More	accurate	high-temperature	models	

•	Faster	design	and	qualificaGon	of	new	materials	

•ContribuGon	to	MII

•	More	accurate	mulG-component	databases	

•	More	efficient	development	of	mulG-component	databases	

•	Faster	and	cheaper	materials	design		
34 Technical Application Areas

Figure 3.2.4 Characterization of Time- and EnvironmentDependent Degradation Phenomena

The objective is to increase the understanding of high-temperature degradation through computational models that

simulate material degradation and through experimentation. This can be achieved by characterizing structural and 

surface degradation, extrapolating short-term tests for life predictions, and reducing inherent complexity and the 

high costs of simulating operations.

Short-term Win

Improve communication between end-users across the 

supply chain

Long-term Advance

Utilize in-service monitoring and inspection data as 

feedback for model development and component 

assessment

Short-term	Ac7vi7es	

•	Establish	communicaGon	protocols	that	protect	

intellectual	property	and	provide	relevant	data		

Long-term	Ac7vi7es	

• PrioriGze	damage	mechanisms	

• Improve	in-situ	non-destrucGve	evaluaGon	and	integrate	

these	measurements	into	materials	and	design	models	

•	Increase	research	into	environment	degradaGon		

Short-term	Milestones	

•	Define	the	business	case	for	data	sharing	

•	Define	a	forum	for	data	sharing	

•	Create	a	mechanism	for	data	sharing		

Long-term	Milestones	

•	List	prioriGzed	mechanisms	

•	Assess	state-of-the-art	and	complete	gap	analyses	

•	Validate	model	predicGons	with	field	data		

Short-term	Outcomes		

•TAA		Benefits

•	Greater	access	to	data	that	supports	predicGve	models	

•	BeOer	next	generaGon	materials	

•ContribuGon	to	MII

•	Common	communicaGon	protocols	

•	Improved	communicaGon	between	end-users	and	

supply	chain	

Long-term	Outcomes	

•TAA		Benefits

•	Validated	predicGve	models	

•	Improved	life	assessments	

•	Improved	materials	development	

•ContribuGon	to	MII

•	More	relevant	applicaGon	data		
Workshop Report: Building the Materials Innovation Infrastructure: 

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35

Figure 3.2.5 Data Capture for High-Temperature Alloys

The time and costs of high-temperature material characterization need to be reduced. These reductions can be 

achieved through activities such as automated data capture and computational models.

Short-term Win

Development of an automated approach to storing data 

and metadata

Long-term Advance

Streamlined and robust data collection for hightemperature alloys

Short-term	Ac7vi7es	

•	Develop	a	user-friendly	automated	approach	for	storing	data	

and	metadata	

•	Define	model-driven	protocols	for	physical		tesGng	

•	IdenGfy	model	materials	that	represent	relevant	classes	of	

mechanisms		

Long-term	Ac7vi7es	

•	Enable	invisible,	zero-cost	data	capture	

•	Enable	model		validaGon	with	dramaGcally	reduced	physical	

tesGng	

•	Develop	accelerated	tesGng	techniques		

Short-term	Milestones	

•	IdenGfy	a	“standard”	material	characterizaGon	project	to	

demonstrate	feasibility	of	data	and	metadata	capture,	storage	

and	retrieval	

•	Develop	an	accelerated	tesGng	protocol	for	a	finite,	standard	set	

of	material	properGes	and	accompanying	physics-based	models	

Long-term	Milestones	

•	Set	up	company	materials	informaGon	system	to	automaGcally	

incorporate	data	from	materials	test	vendor	

•	Achieve	a	50%	reducGon	in	physical	tests	required	for	new	

materials	qualificaGon	

•	Reduce	by	50%	the	Gme	to	measure	all	relevant	properGes	for	

physical	and	virtual	tesGng	techniques	

Short-term	Outcomes		

•TAA		Benefits

•	Reduced	cost	of	data	capture	

•	Reduced	Gme	for	full	characterizaGon	

•ContribuGon	to	MII

•	Transferrable	protocols	to	other	materials	and	applicaGon	

areas		

Long-term	Outcomes	

•TAA		Benefits

•	CommercializaGon	of	new	materials	in	the	Gmeframe	of	design	

process	

•ContribuGon	to	MII

•	Faster	development	of	other	families	of	materials,	

components,	and	assemblies	via	protocols	
36 Technical Application Areas

3.3 CATALYSIS

Catalysis is the change in the rate of a chemical reaction resulting from the use of a catalytic 

agent (catalyst). Catalysts enable reactions that might otherwise be blocked or slowed by a 

kinetic barrier by lowering or altering the requirements for the reaction to take place. Catalysts 

also enable reactions without being consumed in the process. Important sub-classes are 

electrocatalysts and photocatalysts, which enable electrochemical and photochemical conversions 

(e.g., as in fuel cells and devices for sun-driven fuels production). Catalysts already play a large

role in energy and environmental technologies; advances in catalytic materials could enable 

important breakthroughs and lead to important new opportunities.

3.3.1 Overview of Data Challenges for Catalysis

Today challenges in data quality, data management, data representation and data usability are 

hindering progress in the field of catalysis and development of catalytic processes, which could 

provide significant social benefits. The following catalytic processes were identified as those that 

would benefit the most from an improved data infrastructure. Note that this list is not intended 

to be comprehensive; additional feedback from the catalysis community will be needed to ensure 

the most appropriate processes and challenges are being considered.

1. Selective (catalytic) oxidation of methane to methanol (potential for production of 

inexpensive liquid fuels from abundant resources)

2. Biomass conversion to both chemicals and liquid fuels

3. Greenhouse gas conversion

4. Water splitting and oxygen reduction

5. C-C3 feedstock conversion (e.g., shale gas)

The data challenges related to catalytic materials design and development are listed in Table 3.3

and further described in the following sections.

Table 3.3 Catalysis Challenges

(● = one vote)

Data Representation

High 

Priority

• Inconsistent, incomplete structure/activity data/correlations ●●●●●●●●●●●(11)

• Inadequate representation of low symmetry surfaces with adsorbates (ex: amorphous) in 

a searchable format ●●●●●●●●(8)

Medium 

Priority • Lack of a defined list of critical physical descriptors ●●●●●●(6)

Lower 

Priority

• Lack of machine learning tools to derive descriptors

• Description and prediction of microscopic forms (e.g., for oxide catalysts)

• Scaling issues (e.g., scalability) for data and relationships

Data Quality/Pedigree

Medium 

Priority

• Obtaining experimental data on energy of selected systems chosen as well-defined 

benchmarks for extensions via theory ●●●●●●(6)

• New, affordable, and accurate predictive computational methods for designing catalysts 

to benchmark faster models ●●●●●●(6)
Workshop Report: Building the Materials Innovation Infrastructure: 

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37

Table 3.3 Catalysis Challenges

(● = one vote)

Lower 

Priority • Inadequate assessment tools for reliability of experimental and computational data ●●(2)

• Lack of tools for evaluating similarity (dissimilarity) ●(1)

Catalyst Characterization 

High 

Priority

• Lack of descriptions and models of complex materials (e.g., support-catalyst 

interactions); mesoscale processes in particular are poorly modeled/understood 

●●●●●●●●●●●(11)

• Define Industrial focus point around which to apply databases/tools/chemical classes 

●●●●●●●●(8)

• Incomplete documentation of catalyst experimental characterization; standards are 

lacking to enable reproducibility ●●●●●●●●(8)

Medium 

Priority • Lack of available data; raw data (computational and experimental) is not routinely 

provided yet is needed to reproduce results and enable new analyses ●●●●(4)

Lower 

Priority

• Bridging the gaps from computations to surface science to real catalysis ●●●(3)

• Long development times; limited understanding of time requirements and stages of the 

discovery process ●●(2)

• Development of theoretical experimental tools that bridge different length and time 

scales

• Reduce the number of steps involved in modeling kinetics 

Data Sharing

High 

Priority • Lack of experimental methods for high throughput screening ●●●●●●●●●(9)

Medium 

Priority • Inability to incorporate experiments and calculations into a common database (different 

data structures) ●●●●●(5)

Lower 

Priority • Lack of a mechanism or tool to readily interface models with a data repository ●(1)

3.3.2 Top Challenges for Catalysis

Based on the results of the voting process as shown in Table 3.3, a set of the top data challenges 

impacting the design of new materials for catalysis were identified. If addressed these challenges 

have the potential to speed up the materials design cycle. Top challenges are summarized below 

and described in more detail in Figures 3.3.1 through 3.3.5.

Relationships between Fundamental Elements of Data: There is a need to better 

understand the fundamental elements of catalysis data and the relationships within data. 

Improved metadata formats are also needed to enable more advanced searches. The 

development of a comprehensive list of such metadata (loosely described here as a “descriptor”) 

is an important first step to better understanding the data. Addressing these challenges would 

lead to the development of common terminology and data standards for a master list of 

characteristics. Another benefit is improved capabilities for robust design methods of new 
38 Technical Application Areas

catalyst materials s, which would enable discovery and accelerate the development of new 

materials. Figure 3.3.1 provides additional details about this challenge.

High Accuracy Methods for Modeling Catalysis and Reactions: This challenge is related 

to the limitations of commonly used modeling methods that include DFT and QMC, among 

others. While QMC has improved accuracy over DFT methods, it is limited in scope, utility, and 

speed. Improved accuracy of modeling overall would allow for more extensive verification and 

validation of existing methods, yielding potentially revolutionary improvements in accuracy in the 

prediction of difficult applications. High accuracy methods would also enhance capabilities for

benchmarking and validation of models. Figure 3.3.2 provides additional details about this 

challenge.

Mesoscale Catalyst Modeling and Mesoscale Structure-Activity Relationships: The 

increasing complexity of catalyst systems highlights the need for more efficient, reliable 

mesoscale modeling tools that are important to catalysis design. The large magnitude and 

configuration space associated with this challenge requires both new tools and methods for 

materials design. Models are needed that incorporate structural dynamics and electronic 

structures; these can be used as a basis for addressing the durability of catalysts in a fundamental 

way, in addition to other industry-relevant problems. Developing mesoscale models and tools 

could accelerate development of potentially new catalysis systems and would enhance the 

available knowledge base to enable faster development of materials. Figure 3.3.3 provides 

additional details about this challenge.

High Throughput Approaches to Integrated Experimental- and ComputationalDriven Materials Design: It is challenging to integrate experimental and computational data in 

a way that will drive materials design. For example, coupling fast computing with physical models 

and fundamental property data would better enable development of materials to meet energy 

and environmental needs by accelerating the process of catalyst discovery. High throughput 

approaches would increase data sharing and availability and enable the materials science 

community to issue data along with publications. These steps would allow for rapid development 

of new catalysts. Figure 3.3.4 provides additional details about this challenge.

Integrated Predictive Tools for Heterogeneous Catalytic Reaction Mechanisms: The 

materials science community is challenged by the lack of integrated predictive tools for 

heterogeneous catalytic reaction mechanisms. Catalytic reactions are a series of bond-breaking 

and formation steps, the overall rate of which is determined by the catalytic material and 

reaction conditions. For this reason, tools are needed to determine the mechanisms governing 

the critical catalytic transformations and support key predictive experimental and theoretical 

methods - enabling more rapid prototyping of heterogeneous catalytic processes. Figure 3.3.5 

provides additional details about this challenge.
Workshop Report: Building the Materials Innovation Infrastructure: 

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39

Figure 3.3.1 Relationships between Fundamental Elements 

of Data

Identification of the fundamental elements of data (i.e., master list of descriptors) and the relationships between 

elements is key to accelerating materials design and process development. Metadata, such as chemistry and 

structure properties, should be established in a searchable format. 

Short-term Win

Improved screening of new catalyst candidates

Long-term Advance

Quantified structure-property relationships to accelerate 

design and process development

Short-term	Ac7vi7es	

•	ArGculate	a	master	list	of	descriptors	that	includes:	

structure,	reacGvity,	stability,	size,	shape,	and	binding	

energy	

•	Document	the	history	of	catalysts	use	to	idenGfy	

shortcomings	

•	Define	chemical	“neighborhood,”	class,	or	space	

descriptors	and	informaGcally	describe	them	for	screening	

Long-term	Ac7vi7es	

•	Research	correlaGons	among	catalyst	types	and

descriptors and	quanGfy	structure-property	relaGonships	

•	Model	experiments	on	a	chemical	"neighborhood"	for	a	

demonstraGon	classificaGon	

•	Cross-validate	tests	of	chemical	"neighborhoods"	

Short-term	Milestones	

•	IdenGfy	a	list	of	descriptors	agreed	on	by	the	community		

•	Establish	data	representaGon	standards	

•	Agree	on	definiGons	

Long-term	Milestones	

•	IdenGfy	gaps	in	property	data	

•	SystemaGcally	evaluate	composiGon	and	condiGons	to	

address	gaps	in	the	most	important	descriptors	

Short-term	Outcomes		

•TAA		Benefits

•	Common	terminology	

•	Data	standards	and	ontology	for	master	list	of	

characterisGcs	

•ContribuGon	to	MII

•	FoundaGon	for	predicGve	model	development,	gap	

analysis	for	experiments,	and	benchmark	calculaGons	

Long-term	Outcomes	

•TAA		Benefits

•	Robust	predicGon	of	new	catalyst	candidates	

•	Discovery	of	new	materials	with	accelerated	

development	

•ContribuGon	to	MII

•	Framework	for	data	and	insight	lead	to	expansion	of	

knowledge	base	

•	Fundamental	classes	and	understanding	can	be	

generalized	for	other	applicaGons	and	design	
40 Technical Application Areas

Figure 3.3.2 Higher Accuracy Methods for Modeling 

Catalysis and Reactions

The capabilities, accuracy, scope, utility, and speed of DFT and QMC methods related to catalysis development need 

to be improved and expanded.

Short-term Win

Increased usability of existing codes (e.g., Quantum Monte 

Carlo Package (QMCPACK))

Long-term Advance

Improved scope (e.g., solid-state coupled cluster) and 

speed (e.g., better algorithms for all)

Short-term	Ac7vi7es	

•	Increase	amount	of	user-friendly	QMC	codes	

•	Create	more	accessible	GW/Bethe-Salpeter	equaGon	(BSE)	codes	

•	Improve	pseudopotenGals	

•	Improve	funcGonal	dispersion	correcGons	

Long-term	Ac7vi7es	

•	Increase	periodic,	solid-state,	many-body	coupled	cluster	

methods	

•	Improve	scaling	in	system	size	(e.g.,	local	methods)	

•	Hybridize	codes	and	methods	

•	InvesGgate	trade-off	between	accuracy	and	speed	with	

algorithms	and	theoreGcal	development	

Short-term	Milestones	

•	AdopGon	by	users	(outside	of	developers)	of	QMC	GW/BSE	

•	Create	high-level	reference	data	for	developing	funcGonal	

potenGals,	pseudopotenGals,	and	web	database	with	

documentaGon	

Long-term	Milestones	

•	Apply	coupled	cluster	methods	to	solids	to	possibly	show	proofof-principle	

•	Create	a	periodic	100	atom	system	(crystal)	with	coupled	cluster	

with	single	and	double	excitaGon	

•	Converge	diffusion	Monte	Carlo	on	100	atoms	(solid/periodic)	

with	less	than	one	week	compuGng	Gme	

•	Develop	carbon	monoxide	and	water	on	plaGnum(III)	to	within	

10	to	20	millielectron	volt	with	any	method	

Short-term	Outcomes		

•TAA		Benefits

•	Increased	use	of	beOer	methods	within	the	community	

•More	extensive	verificaGon	and	validaGon	in	exisGng	DFT/

pseudopotenGals	

•ContribuGon	to	MII

•	More	powerful	methods	generally	applied	to	materials	

problems	

Long-term	Outcomes	

•TAA		Benefits

•	RevoluGonary	accuracy	in	predicGon	of	very	hard	applicaGons	

•	Benchmarking	and	validaGon	of	models	

•ContribuGon	to	MII

•	Faster,	more	accurate	materials	development		
Workshop Report: Building the Materials Innovation Infrastructure: 

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41

Figure 3.3.3 Mesoscale Catalyst Modeling and Mesoscale 

Structure-Activity Relationships

Mesoscale modeling tools can be improved by including structural dynamics as well as electronic structure to enable 

high fidelity simulation for catalysis and materials discovery. 

Short-term Win

New mesoscale models and experimental tools with 

temporal and spatial resolution

Long-term Advance

Established knowledge base and tool set for accelerating 

mesoscale material design

Short-term	Ac7vi7es	

•	IdenGfy	model	systems	for	mesoscale	model	development	and	

experimental	calibraGon	

•	Establish	working	groups	to	bridge	atomisGc	and	microscale	

modeling	for	new	methodologies	

•	Implement	experimental	tools	(e.g.,	transmission	electron	

microscope	,	X-ray)	for	inoperando	mesoscale	measurements		

Long-term	Ac7vi7es	

•	Define	error	propagaGon	for	higher-order	system	

•	Establish	infrastructure	for	users	

•	Contribute	assessments	and	define	metrics	for	quality	

assessment		

Short-term	Milestones	

•	Illustrate	model	system	with	idenGfied	data	constructs	for	future	

system	implementaGon	

Long-term	Milestones	

•	Create	accessible	knowledge	base	for	industrially	relevant	

catalyGc	processes	

Short-term	Outcomes		

•TAA		Benefits

•	CreaGon	of	a	group	of	models	to	be	used	as	a	basis	for	tackling	

industry-relevant	problems	

•	Ability	to	address		durability	in	a	fundamental	way	

•ContribuGon	to	MII

•	Capacity	to	bridge	more	established	scales	(e.g.,	macro	and	

atomisGc)	with	a	new	level	of	modeling		

Long-term	Outcomes	

•TAA		Benefits:	

•	FormaGon	of	new	material	systems	for	catalysis	

•	Broadened	available	knowledge	base	for	faster	development	

•ContribuGon	to	MII

•	ConGnued	ability	and	infrastructure	to	bridge	scales	
42 Technical Application Areas

Figure 3.3.4 High Throughput Approaches to Integrated 

Experimental- and Computational-Driven Materials Design

Effective integration of experimental and computational data can accelerate catalyst discovery by taking advantage of 

fast computing, physical models, and fundamental property data.

Short-term Win

Rapid identification of potential new catalytic materials

Long-term Advance

Expanded and improved quality of fundamental data 

infrastructure needed to amplify and accelerate the 

successful discovery of new catalytic materials

Short-term	Ac7vi7es	

•	IdenGfy	“descriptors”	for	key	reacGons	

•	Correlate	exisGng	materials	composiGon	and	structure	with	

descriptor	values	

•	Validate	choice	of	descriptor	

•	Carry	out	a	computaGonal	search	for	new	materials	which	meet	

criteria	for	a	promising	catalyst	target		

Long-term	Ac7vi7es	

•	Obtain	experimental	data	on	selected	benchmark	systems	which	

enable	vast	extension	via	computaGonal	theory	

•	Develop	methods	for	high	throughput	generaGon	of	

experimental	data	

•	Improve	methods	for	finding	predictors	

•	Develop	faster	computaGonal	methods	that	have	higher	

accuracy	

Short-term	Milestones	

• Descriptors	for	key	reacGons	

•	Demonstrated	computaGonal	search	for	new	materials	based	on	

selected	criteria	

Long-term	Milestones	

•	Validated	high	throughput	methods	for	experimental	data	

generaGon	

•	DemonstraGon	of	higher	accuracy	using	selected	benchmarks	

Short-term	Outcomes		

•TAA		Benefits

•	Increased	data	sharing	and	availability	

•	Increased	data	disclosure	with	publicaGons	

•ContribuGon	to	MII

•	Integrated	system	containing	validated	data		

Long-term	Outcomes	

•TAA		Benefits

•	Rapid	development	of	new	catalysts	

•ContribuGon	to	MII

•	Sustained	system	for	integraGon	of	validated	data		
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43

Figure 3.3.5 Integrated Predictive Tools for 

Heterogeneous Catalytic Reaction Mechanisms

The mechanisms governing critical catalytic transformations need to be established. The identification of key 

predictive experimental and theoretical tools will be essential to this effort.

Short-term Win

Establishment of simple qualitative descriptors from 

known reactions that permit rapid prediction of candidate 

catalysts

Long-term Advance

Increased comprehension of reaction mechanisms, 

including those with complex aspects

Short-term	Ac7vi7es	

•	Establish	set	of	qualitaGve	descriptors	from	known	reacGons	

that	permit	rapid	predicGon	of	candidate	catalysts	for	a	specific	

class	of	reacGons	and	related	new	classes	of	reacGons	

•	Analyze	and	validate	exisGng	data	to	determine	descriptors	and	

create	informaGcs	tools	for	data	validaGon	and	analysis	

•	Extend	the	applicaGon	of	the	design	concept	to	more	complex	

reacGons	

•	Refine	the	descriptors	for	increased	accuracy		

Long-term	Ac7vi7es	

•	Provide	new	tools	and	approaches	for	decreasing	development	

and	prototyping	Gme	frame	

•	Expand	design	tools	to	be	more	comprehensive	and	include	

complexity	of	materials	and	reacGon	condiGons	

•	Secure	stable	resources	for	computer-based	projects		

•	Engage	industry	

Short-term	Milestones	

•	Demonstrate	proof	of	principle	that	descriptors	in	a	model	

system	lead	to	improved	catalyGc	performance	for	a	specific	

reacGon	

Long-term	Milestones	

•	Sustained	development	of	validated	predicGve	tools	for	reacGon	

mechanisms	

Short-term	Outcomes		

•TAA		Benefits	

•	Rapid	prototyping	of	heterogeneous	catalyGc	processes	

•ContribuGon	to	MII	

•	Data	infrastructure	for	reacGon	mechanisms	

Long-term	Outcomes	

•TAA		Benefits	

•	DeterminaGon	of	mechanisms	for	catalyGc	reacGons	using	

more	complex	materials,	including	mulG-component,	noncrystalline	and	porous	frameworks	

•	Greater	understanding	of	reacGon	mechanisms	and	the	

evoluGon	of	catalyGc	materials	under	a	range	of	condiGons,	

including	complex	environments	

•ContribuGon	to	MII	

•Data	infrastructure	for	reacGon	mechanisms	
44 Technical Application Areas

3.4 LIGHTWEIGHT STRUCTURAL MATERIALS

Lightweight structural materials enable industrial, commercial, and transportation systems to 

push performance limits and often times exceed customer expectations while matching or 

improving energy efficiency, safety, durability, utility, and/or environmental goals. For example, 

novel alloys based on magnesium and on titanium aluminides are lightweight and have promising 

property profiles that could make them suitable for certain applications, such as the power trains 

of automobiles or structural components made from cast and wrought alloys. 

3.4.1 Overview of Data Challenges for Lightweight Structural 

Materials

The data challenges related to design and development of lightweight structural materials are 

listed in Table 3.3 and further described in the following sections.

Table 3.4 Lightweight Structural Material Challenges

(● = one vote)

Specific to Lightweight Structural Materials

High 

Priority

• Establishing a data repository(s) for aluminum, titanium, and magnesium ●●●●●●●●●●●● (12)

• Developing models of structures or materials comprising multiple materials, such as fiber plus

matrix in a composite; two metals plus weld; or single crystal plus precipitate in a

polycrystalline structure ●●●●●●●●●● (10)

• Developing phase and interface properties, homogenization theories/models ●●●●●● (6)

• Modeling joints ●●●●●● (6)

Medium 

Priority • Lack of good data on elastic coefficients ●●●(3)

Lower 

Priority

• Understanding how constituent material properties relate to a specific (laminate) orientation

(i.e., in a composite structure) ●●(2)

• Establishing preferred orientation for texture ●●(2)

• Modeling processes to convert material to usable goods

Generic to Lightweight Structural Materials

High 

Priority • Developing standards for defining data quality “data readiness level” ●●●●●●●●●(9)

Medium 

Priority

• Drive data cost to zero; create, manage, explore ●●●●●(5)

• Creating data "social networks” ●●●●●(5)

– Data and models: what’s available and who’s willing to share?

• Establishing an expert rating system for data and data reproducibility ●●●●(4)

• Establishing ownership and maintenance of the MGI database ●●●●(4)

• Lack of taxonomy, e.g., a rules-based classification system for items across disciplines, length 

scales, experiments, and analyses ●●●(3)

Lower 

Priority

• High cost behind data – unique or expensive data not available to potential users ●●(2)

• Filtering data (i.e., one kind of information);combining targeted data across domains ●●(2)

• Creating a large data network that is transparent, self-organizing, and flexible ●(1)

• Understanding model readiness level ●(1)
Workshop Report: Building the Materials Innovation Infrastructure: 

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45

3.4.2 Top Challenges for Lightweight Structural Materials

Based on the results of the voting process as shown in Table 3.4, a set of the top data challenges 

impacting the design of new lightweight structural materials were identified. If addressed these 

challenges have the potential to speed up the materials design cycle. Top challenges are 

summarized below and described in more detail in Figures 3.3.1 through 3.3.5.

Optimized Joining Methods: Multi-scale models are needed to determine optimized joining 

methods for lightweight materials. These models would predict material performance in areas 

such as corrosion susceptibility and rates, static strengths, fatigue, and dynamic strength. Initial

multi-scale models development could begin with selected materials and joining processes. New 

and continuously updated validation data will also be needed to ensure optimal model outputs.

Development of good multi-scale models for joining will improve the ability to design durable 

hybrid structures with disparate materials bonding profiles. Figure 3.4.1 provides additional 

details about this challenge.

Models to Connect Phase Properties: To create structures of different sizes and shapes, 

engineers need to know bulk properties, which depend strongly on phase properties, topology, 

and interfaces. Today, inadequate models exist that can connect the bulk with phase properties. 

Development of these models will enable predictive properties capabilities that could be utilized 

by both experts and non-experts involved in materials design and development. Figure 3.4.2 

provides additional details about this challenge.

Data Repositories for Aluminum, Titanium, and Magnesium: Aluminum, titanium, and 

magnesium are predicted to be the engineering workhorses for many structural applications in 

the future due to their high strength to weight properties. These material systems comprise a 

multitude of alloy types and configurations. Currently, a robust data repository for each alloy 

type does not exist, so non-validated data and information is sometimes used for model 

development and potentially structure design. An engineering performance data repository 

would provide a source of reliable data to facilitate high confidence part designs. Figure 3.4.3 

provides additional details about this challenge.

Large Reduction in Data Costs: Materials data should be available to the materials 

community of interest as well as the broader scientific and industrial community, and should be 

increased by orders of magnitude to create a high fidelity MII. Creating, collecting, and storing 

potentially immense amounts of data is expensive. It is a significant challenge exists to drive these 

data costs down, but it will be essential to creating a robust MII. Figure 3.4.4 provides additional 

details about this challenge.

Standardized Definitions for Maturing Levels of Computational Models, 

Experimental Data, and Simulation: A standard set of data readiness levels should be 

developed to characterize data and models and to provide an assessment of the fidelity of 

collected information which can be used in various decision-making circumstances. These 
46 Technical Application Areas

metrics, similar to the technology readiness levels used by the U.S. Department of Defense 

(DOD) and the National Aeronautics and Space Administration, would characterize the maturity 

of data or models. This could be factored into a structure’s design. Figure 3.4.5 provides 

additional details about this challenge.

Multi-Material Models that include Texture and Structure: Today’s materials modeling 

techniques that take into consideration texture with structure are inadequate or non-existent. 

This capability is extremely difficult to achieve and why it is absent or limited. However, 

elucidating computational methods to achieve such a linkage could open up new design space for 

lightweight materials and other material systems. Figure 3.4.6 provides additional details about 

this challenge.
Workshop Report: Building the Materials Innovation Infrastructure: 

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47

Figure 3.4.1 Optimized Joining Methods

To optimize joining methods, the relevant properties (e.g., static, fatigue, dynamic strength, and corrosion 

resistance) of joined materials must be identified. The development of multi-scale models will enable prediction of

bonding possibilities, bonding strength, and optimized joining methods between new materials. The data must be 

continuously validated and updated for on-going model optimization.

Short-term Win

Use of data and models to design joints for parts in a 

variety of industries

Long-term Advance

Creation of multiscale models to design and predict the 

joining of disparate materials

Short-term	Ac7vi7es	

•	IdenGfy	materials	and	joining	processes	likely	to	lead	to	

significant	cost	and	weight	savings	

•	Understand	convenGonal	state-of-the-art	approach	to	problem	

by	others		

•	Define	objecGves	and	new	framework	

•	Ascertain	technical	gaps		

•	Determine	naGonal	return	on	investment	

Long-term	Ac7vi7es	

•	Create	a	repository	of	joint	performance	data	that	includes	

variability	of	in	bound	material	and	process	

•	Standardize	techniques	for	joint	tesGng	

•	Validate	models	at	wide	range	of	length	scale	

•	Verify	model	across	a	suitable	range	of	materials,	processes,	and	

scales	

Short-term	Milestones	

•	IdenGfy	specific	(representaGve)	joint	geometries	and	materials	

•	Define	state-of-the-art	for	joint	opGmizaGon	

•	Characterize	new	framework	(high-level)	

Long-term	Milestones	

•	Create	a	repository	of	joint	performance	under	relevant	service	

condiGons	

•	Standardize	techniques	for	joint	tesGng	aVer	being	placed	in	

relevant		service	condiGons	

•	Develop	an	expert	system	for	predicGng	joint	variability	

Short-term	Outcomes		

•Benefit	to	TAA			

•	BeOer	models	for	exisGng	joint	and	material	configuraGons	

•	Faster	implementaGon	of	lightweight	materials	

•ContribuGon	to	MII	

•	Development	of	mulG-scale-aware	framework	for	improving	

exisGng	major	industry	sector	

•	ConnecGon	of	material	and		design	to	end	performance	

Long-term	Outcomes	

•Benefit	to	TAA		

•	Faster	more	dexterous	use	of	broader	range	of	materials	

•	Improved	reliability	through	beOer	corrosion	performance	

•	ReducGon	in	weight	and	cost	by	improved	confidence	in	joint	

strength	data	

•Contribu7on	to	MII

•	ResoluGon	of	key	issues	over	a	broad	range	of	scales	in	joint	

design	and	durabilty	(e.g.,	bonding	is	at	molecular	level,	

corrosion	at	microstructure	level,	faGgue	at	all	scales,	staGc	

strength	at	macro	but	driven	by	micro)	
48 Technical Application Areas

i

Figure 3.4.2 Models to Connect Phase Properties

Models that link bulk properties with phase, topology, and interface properties can aid in quantitative prediction of 

component performance.

Short-term Win

Identification of important factors connecting phase 

properties with interface properties

Long-term Advance

Quantitative prediction of component performance to 

accelerate design process based on a complete 

understanding of phase and interface properties

Short-term	Ac7vi7es	

• Generate	phase	properGes

•	Encourage	awareness	and	understanding	that	orientaGon	

is	comparable	in	importance	to	phase	distribuGons	in	

determining	the	properGes	of	magnesium	oxide,	Gtanium,	

and	aluminum-lithium	

•	Establish	twin	interface	(e.g.,	magnesium,	Gtanium,	

Gtanium-aluminum,	precipitaGon,	hardening,	fracture)	

Long-term	Ac7vi7es	

•	Enable	design	engineers	to	quanGtaGvely	predict	

component	performance	and	properGes	using	

microstructure	variables	and	phase	aOributes	

•	Facilitate	quanGtaGve	predicGon	of	aggregate	properGes	

based	upon	phase,	interface,	etc.	

•	Use	interface	properGes	(e.g.,	energies,	diffusiviGes)	in	

design	as	a	degree	of	freedom	

Short-term	Milestones	

•	IdenGfy	specific	coarse-graining	(homogenizaGon)	

approaches	most	promising	for	MGI	

•	IdenGfy	missing	properGes

•	IdenGfy	strategies	to	obtain	properGes	at	phases,	not	

normally	obtainable	as	single	crystals	(e.g.,	Guinier	and	

Preston	zones)	

Long-term	Milestones	

•	Obtain	community	acceptance	of	orientaGon	as	

comperable	in	importance	phase	distribuGons	for	

magnesium	oxide,	Gtanium,	and	aluminum-lithium		

•	Interface	mechanism	in	computaGonal	modeling	(e.g.,	

Finite	Element	Analysis)	and	not	just	cohesive	zone	

•	Develop	methods	(e.g.,	models,	experts)	to	quanGfy	

interface	properGes	over	huge	orientaGon	space	

Short-term	Outcomes		

•Benefit	to	TAA			

•	Move	from	wizard,	experience,	and	empirical	to	

quanGtaGve	and	predicGve	descripGon	of	impact	of	

phase,	interface,	and	topology	properGes	on	bulk	

properGes	

•ContribuGon	to	MII	

•	IdenGficaGon	of	important	quanGGes	for	phase	and	

interface	properGes	

Long-term	Outcomes	

•Benefit	to	TAA			

•	Faster,	cheaper	designs	of	lightweight	structures	by	

including	phase,	interface,	and	topology	as	design	

variables	

•ContribuGon	to	MII	

•	IdenGficaGon	of	methods	for	incorporaGng	phase	and	

interface	properGes	into	higher	scale	models	(integraGon	

techniques	and	models)	
Workshop Report: Building the Materials Innovation Infrastructure: 

Data and Standards

49

Figure 3.4.3 Data Repositories for Aluminum, Titanium, 

and Magnesium

A performance data repository needs to be created for the workhorse engineering materials (i.e., aluminum, 

titanium, and magnesium). This will improve capabilities for alloy model development. 

Short-term Win

Definition of scope, funding, needs, and logistics of data 

repository

Long-term Advance

Establishment of repository (i.e., models with validated 

data) as primary data collection for efforts in light metals

Short-term	Ac7vi7es	

•	Define	data	to	be	included	

•	Secure		resources	for	development	of	repositories	

•	Enable	Internet	access	across	mulGple	databases	

•	IdenGfy	where	the	data	should	reside	

Long-term	Ac7vi7es	

•High	fidelity	models	of	materials	(e.g.,	chemistry,	micro,	and	

properGes)	that		are	depenendent	on	composiGon,	temperature,	

pressure		

•	Acquire	fundamental	“data”	for	all	binary	and	ternary		systems	

in	aluminum,	Gtanium,	and	magnesium	

•	Validate	models	with	descripGons	as	inputs	and	experiments		

Short-term	Milestones	

•	IdenGfy	and	explore	current	sites	(e.g.,	ASM	InternaGonal's	

Medical	Materials	Database)	

•	Demonstrate	single	point	search	

•	Establish	a	flexible	protocol/schema	that	builds	on	exisGng	work	

•	Establish	a	community	of	interest	via	incenGves	and	other	

mechanisms	

Long-term	Milestones	

•	Demonstrate	model	uGlity	for	industrial	and	scienGfic	

sector	problems	

•	Establish	repository	as	primary	data	collecGon	for	efforts	

in	light	metals	

Short-term	Outcomes		

•Benefit	to	TAA			

•	Save	money	by	avoiding	duplicaGon	

•	Tighter	coupling	between	“academia”	and	industry	

•	PotenGal	performance/cost	benefit	to	U.S.	industry	

•ContribuGon	to	MII	

•	Success	of	pilot	project	exemplar	demonstrated	for	

other	areas	

Long-term	Outcomes	

•Benefit	to	TAA			

•	Pervasive	use	of	light	metals	

•	Establishment	of	community	repository	

•	Perpetual,	self-sustained	system	

•ContribuGon	to	MII	

•	None	idenGfied	
50 Technical Application Areas

Figure 3.4.4 Large Reduction in Data Costs

Data costs should be driven toward zero while collecting significantly more technical data. Less costly tools for data 

generation will be needed to accomplish this. Social networks and data repositories could also provide greater 

access of data and tools to communities of interest. 

Short-term Win

Creation of more usable and relevant data for users to 

analyze

Long-term Advance

Enabling a more in-depth material understanding while 

lowering the cost to store data

Short-term	Ac7vi7es	

•	Map	major	data	types	used	in	lightweight	structural	materials		

•	EsGmate	costs	for	creaGng,	managing,	and	exploring	the	data	

types	

•	Issue	challenges	to	reduce	by	at	least	10x	the	cost	to	create	

data,	for	lightweight	structural	materials	for	example	

•	Create	social	network	and	data	repository	to	share	data	and	

foster	collaboraGon	

Long-term	Ac7vi7es	

•	Create	new	simulaGon	and	predicGve	models	for	data	

producGon	at	low	cost	

•	Miniaturize	exisGng	physical	data	creaGon	techniques	

Short-term	Milestones	

•	Write	report	of	data	types	and	classes	used	in	lightweight	

structural	materials	field	

•	Segment	data	generators	and	obtain	cost	models	

•	IdenGfy	challenges	with	prizes	to	change	data	cost	structure	

•	Launch	social	network	with	data	repository	and	measure	data	

flow	and	user	parGcipaGon	

Long-term	Milestones	

•	Release	simulaGon	and	predicGve	models	and	tools	for	general	

use	

•	Create	new	automated	equipment	to	produce	high	quality	data	

for	years	

•	Launch	social	network	for	lightweight	structural	materials	data	

sharing,	challenges,	and	partnering	

Short-term	Outcomes		

•Benefit	to	TAA			

•	More	data	for	analysis	

•	Engagement	of	nontradiGonal	experts	and	disciplines	

•ContribuGon	to	MII	

•	Generalized	method	for	data	creaGon	at	greatly	reduced	cost	

•	FormaGon	of	a	socially	networked	environment	for		data	

creaGon	and	knowledge	discovery	

Long-term	Outcomes	

•Benefit	to	TAA			

•	More	well	documented	material	properGes	for	design	and	

manufacturing	applicaGons	

•ContribuGon	to	MII	

•	Establishment	of	general	methods	and	environment	to	

accelerate	discovery	of	novel	materials,	new	material	

applicaGons,	and	knowledge	network	formaGon	
Workshop Report: Building the Materials Innovation Infrastructure: 

Data and Standards

51

Figure 3.4.5 Standardized Definitions for Maturing Levels 

of Computational Models, Experimental Data, and 

Simulation

The establishment of data and model readiness levels (DMRL), similar to the technology readiness levels used by 

DOD and NASA, would aid in characterization of data and model readiness for decision-making. 

Short-term Win

Identification of a DMRL for use in lightweight materials 

design

Long-term Advance

Identifies the areas of lightweight materials data and 

models that require greater confidence

Short-term	Ac7vi7es	

•	IdenGfy	and	gather	a	community	of	interest	

•	Define	strawman	DMRLs	

•	Present	draV	to	commiOee	

•	Iterate	draVs	with	community	and	commiOee	

Long-term	Ac7vi7es	

•	Publicize	existence	of	these	metrics	

•	Promote	usage	of	DMRLs	in	relevant	funding	and	sponsorship	

opportuniGes	

Short-term	Milestones	

•	IdenGfy	community	

•	Hold	kick-off	meeGng	

•	DraV	DMRLs	

Long-term	Milestones	

•	Gain	widespread	adopGon	of	DMRLs	throughout	industry	and	

government	

Short-term	Outcomes		

•Benefit	to	TAA			

•	Facilitates	common	understanding	of	model	and	data	readiness	

for	lightweight	materials	

•ContribuGon	to	MII	

•	ApplicaGon	of	same	metrics	throughout	MII	

•	FacilitaGon	of	data	and	model	usage	for	innovaGon	

Long-term	Outcomes	

•Benefit	to	TAA			

•Same	as	short-term	outcomes	

•ContribuGon	to	MII	

•Same	as	short-term	outcomes	
52 Technical Application Areas

Figure 3.4.6 Multi-material Models that Include Texture 

and Structure

Beneficial improvements to materials models include relating structure performance to texture, grain morphology, 

constituent particles, and other structural data to reveal non-obvious performance factors (e.g., anisotropy).

Short-term Win

Identification of relationships between materials’ 

interfaces and structural performance

Long-term Advance

Creation of models for predicting composite structures 

based on constituent materials properties

Short-term	Ac7vi7es	

•	Perform	a	literature	survey	to	determine	state	of	the	art	

relaGonships	between	interfacial	structure	and	structural	

performance	

•	Establish	detailed	tesGng	and	analysis	for	state	of	the	art	

•	Determine	domains	of	applicability	

•	Expand	domains	to	materials	such	as	composites,	metals,		and	

polycrystal	line	structures	

Long-term	Ac7vi7es	

•	Determine	important	vehicle	properGes	

•	Determine	consGtuent	properGes	relevant	to	vehicle	

performance	

•	Establish	a	consGtuent	link	to	the	aggregate	properGes	

•	Develop	required	data	sets	at	all	relevant	length	scales	to	inform	

materials	decisions	

Short-term	Milestones	

•	Create	state	of	the	art	report	

•	Generate	intermediate	characterizaGon	of	interface	influence	on	

tensile	strength	for	one	material	

•	Use	new	informaGon	to	design	interface	

•	Perform	interface	tests	

•	Establish	data	format	for	modeling	codes.	

Long-term	Milestones	

•	Determine	final	vehicle	performance	criteria	

•	Establish	final		needed	consGtuent	propertes

•	Develop,	refine,	and	validate	consGtuent	models	

•	Disseminate	to	community	

Short-term	Outcomes		

•Benefit	to	TAA			

•	Database	on	interface	influence	

•	New	best	pracGce	interface	design	

•ContribuGon	to	MII	

•	Specific	contribuGons	to	MII	database		

•	Template	for	user	pracGce	for	data	input	to	MII	

Long-term	Outcomes	

•Benefit	to	TAA			

•	Reduced	cost	of	development	of	new	materials	

•ContribuGon	to	MII	

•	GeneraGon	of	data	for	database	

•	CreaGon	of	consGtuGve	model	
Workshop Report: Building the Materials Innovation Infrastructure: 

Data and Standards

53

4 CROSS-CUTTING CHALLENGES

A number of cross-cutting themes can be found throughout this report. In many cases these 

challenges impact the entire community involved in materials design. Some of the leading themes 

are summarized below.

Strong leadership for community efforts: Strong community leadership will be needed to 

foster the level of data generation, analysis, and sharing needed to support a successful MII. The 

materials community includes many disciplines and applications and these groups of interest can 

be somewhat isolated. As a result, there is limited leadership to support or champion efforts that 

will benefit the community at large. A cultural change towards a data-sharing philosophy will

require leadership to build community-wide support and understanding of the value proposition. 

This will continue to be a challenge but is vital to the success of the future MII. 

Data sharing: Incentives and structures are needed to encourage data sharing. Mechanisms are

currently lacking to balance the needs of organizations and the larger community for sharing and 

distributing data. Both public and private organizations can require ownership of certain results 

and data for scientific or business reasons. Mechanisms are needed to balance those needs with 

the broader interests of the community while protecting ownership of discoveries, intellectual 

property, and competitive differentiators. A reward system for shared datasets, coupled with a 

structure that protects data at some level or credits data, could provide incentives for data 

dissemination. Digital Object Identifiers (DOI®) could be important idea for sharing data. These 

create a framework for identification, managing intellectual content and metadata, linking users

with content sources, and enabling automated management of media.8,9

Computational validation: Validation of computational models was cited across all length 

scales as well as applications as a high priority. Computational validation tools require a long time 

to develop and are a general impediment to the success of the MII. Systematic and proper 

evaluation of the data acquisition process (and data) for model validation is another key 

challenge. Of particular interest is the ability to measure and prove that validated computational 

tools can greatly speed the design of new materials; be effectively applied to ‘real’ products; and 

reduce time and cost of testing materials.

Central data repository: The need for centralized and accessible data is a common challenge 

cited by all groups and noted as a high priority. Such repositories are currently limited and lack

 

9 http://www.doi.org/
54 Cross-cutting Challenges

standard formats which makes it more difficult for data to be used by the community at large. 

Data management in general can be costly, requiring resources not just for generation but for 

collection, archiving, and maintenance. Some of the general requirements for a central data 

repository include:

• Standardized formats

• Definition of what data should be stored and maintained

• Data standards for uploading data

• Extensible framework for new data types that emerge

• Usable by non-materials scientists

• Means for sustained data life and maintenance

A HUB-based infrastructure serving a large number of users is one approach. In this case users 

could have access to a standard repository as well as opportunities and support for interacting 

with data. Similar successful efforts (e.g., nanoHUB) could be explored to gain insights on how to 

build and effectively operate a HUB-based repository.

Large data sets: The lack of methods for proper storage, transmission, and analysis of 

extremely larger datasets was noted as a cross-cutting challenge for a number of areas. The 

amount of data generated and accessible is growing exponentially and this trend is expected to 

continue. New methods will be needed to effectively manage and extract useful information 

from these massive datasets. 

Data interfaces and interoperability: Effectively using available data is challenged by a lack 

of open source interfaces and interoperability between databases. Resolving these issues is a high 

priority for all domains. The lack of metadata interfaces between databases is a major issue, along 

with the ability to combine experimental and computational data and computations from 

different sources. Translating to different formats to interface with different software applications 

is currently problematic. Flexible, common data schemas and interfaces are needed to resolve 

some of these challenges. Other requirements for data compatibility include integration of 

multiple property datasets in a single searchable environment and compatibility with commercial 

codes. 

Data standardization: Stringent standards are needed for both computational and 

experimental data are considered a high priority, including data curation and a data quality index 

to describe the quality of specific properties based on testing conditions. Standard data formats 

and metadata requirements for reporting and databasing of raw test data are also a priority; 

these will greatly facilitate data sharing and understanding of data quality and context.
Workshop Report: Building the Materials Innovation Infrastructure: 

Data and Standards

55

APPENDIX A: PARTICIPANT LIST

Plenary Speakers

Axel Szalay, Johns Hopkins University

Gerhard Klimeck, Purdue University

Ian Foster, Argonne National Lab; University Chicago

Mladen Vouk, North Carolina State University

Workshop and Report Preparation

Energetics Incorporated

Sean Agnew University of Virginia

John Allison University of Michigan

Clare Allocca National Institute of Standards and Technology

Steven Arnold National Aeronautics and Space Administration

Michael Ashby University of Cambridge

Mark Asta University of California, Berkeley

Rick Barto Lockheed Martin Advanced Technology Laboratories

Laura Bartolo Kent State University

Chandler Becker National Institute of Standards and Technology

Amber Boehnlein Stanford Linear Accelerator Laboratory

Ronald Boisvert, National Institute of Standards and Technology

Donald Boyce Cornell University

Mary Brady National Institute of Standards and Technology

Lisa Brasche Iowa State University

Curt Breneman Rensselaer Polytechnic Institute

Charles Campbell University of Washington, Department of Chemistry

Carelyn Campbell National Institute of Standards and Technology

Tof Carim U.S. Department of Energy, Office of Science and Technology Policy

Gerbrand Ceder Massachusetts Institute of Technology

Anne Chaka Pacific Northwest National Laboratory

Long-Qing Chen Penn State University

John Christensen Robert Bosch LLC

Stephen Christensen Boeing Research & Technology

Julie Christodoulou Office of Naval Research

James Cotton Boeing

Paul Crooker Electric Power Research Institute

Thomas Devereaux Stanford Linear Accelerator Laboratory

Andrew Dienstfrey National Institute of Standards and Technology

Michael Fahrmann Haynes International, Inc.

Barry Farmer Air Force Research Laboratory
56 Appendix A: Participant List

Tim Finin University of Maryland, Baltimore County

Ian Foster University of Chicago; Argonne National Laboratory

Stephen Freiman Freiman Consulting

Cynthia Friend Stanford Linear Accelerator Laboratory

Martin Fritts National Institute of Standards and Technology

Marc Fry Granta Design

David Furrer Pratt & Whitney

Edwin Garcia Purdue University

Bruce Garrett Pacific Northwest National Laboratory

Edward Glaessgen National Aeronautics and Space Administration, Langley Research 

Center

Sharon Glotzer University of Michigan

Carlos Gonzalez National Institute of Standards and Technology

Jeffrey Greeley Argonne National Laboratory

Dan Gunter Lawrence Berkeley National Laboratory

Francois Gygi University of California, Davis

Jeff Hammond Argonne National Laboratory

Britt Hedman Stanford Linear Accelerator Laboratory

Scott Henry ASM International

Daryl Hess National Science Foundation

Michael Hill University of California, Davis

Hanchen Huang University of Connecticut

Kerry Hughes The Dow Chemical Company

Warren Hunt The Minerals, Metals & Materials Society

Patrick Hurley Johnson Controls

Russell Irving General Electric Company

Greg Jackson University of Maryland

Duane Johnson Ames Laboratory

William Joost U.S. Department of Energy

Surya Kalidindi Drexel University

Ursula Kattner National Institute of Standards and Technology

Steven Kaye Wildcat Discovery Technologies

Robert Kee Colorado School of Mines

John Kieffer University of Michigan

John Kitchin Carnegie Mellon University

Gerhard Klimeck Purdue University

William Knowlton Boise State University

Boris Kozinsky Robert Bosch LLC

Paul Krajewski General Motors Company

Charles Kuehmann QuesTek Innovations LLC

Sergey Levchenko Fritz Haber Institute of Max Planck Society

Alexis Lewis U.S. Naval Research Laboratory

Mei Li Ford Motor Company

Kenny Lipkowitz Office of Naval Research

Ping Liu Brookhaven National Laboratory
Workshop Report: Building the Materials Innovation Infrastructure: 

Data and Standards

57

Zi-Kui Liu The Pennsylvania State University

Miron Livny Wisconsin Institutes for Discovery

Benjamin Mann Ayasdi, Inc

Radenka Maric University of Connecticut

David Marques Elsevier

Emmanuelle Marquis University of Michigan

Will Marsden Granta Design

David Martinsen American Chemical Society

Paul Mason Thermo-Calc Software Inc

Matthew Miller Cornell University

Bhubaneswar Mishra Courant Institute of Mathamatical Sciences at New York University

Amy Moll Boise State University

Charles Moosbrugger ASM International

Dmitri Novikov United Technologies Research Center

Vidvuds Ozolins University of California, Los Angeles

Clare Paul Air Force Research Laboratory

Kristin Persson Lawrence Berkeley National Laboratory

Tresa Pollock University of California, Santa Barbara

Mohamed Rahmane General Electric Company, Global Research

Krishna Rajan Iowa State University 

Jud Ready Exponent

John Rodgers Innovative Materials Technologies Inc.

Gregory Rohrer Carnegie Mellon University

Robert Rudd Lawrence Livermore National Laboratory

Joseph Salvo General Electric Company, Global Research

Nanda Santhanam Autodesk

Paul Saxe Materials Design, Inc.

Sadas Shankar Intel Corporation

Jeff Simmons Air Force Research Laboratory

David Skinner Lawrence Berkeley National Laboratory

Lewis Sloter US Department of Defense

George Spanos The Minerals, Metals & Materials Society

Ram Sriram National Institute of Standards and Technology

Ranjan Srivastava University of Connecticut

Alejandro Strachan Purdue University

Veera Sundararaghavan University of Michigan

Alexander Szalay Johns Hopkins University

Sam Thamboo General Electric Company

Ward Thomas Sentient Corporation

Katsuyo Thornton University of Michigan

Sally Tinkle Nano Science and Technology Institute, National Nanotechnology 

Coordination Office

Dallas Trinkle University of Illinois, Urbana-Champaign

Anton Van der Ven University of Michigan
58 Appendix A: Participant List

Krystyn Van Vliet Massachusetts Institute of Technology

Gregory Voth The University of Chicago

Mladen Vouk North Carolina State University

Charles Ward Air Force Research Laboratory

James Warren National Institute of Standards and Technology

Charles Welch U.S. Army, Engineer Research and Development Center

Terry Wong Pratt & Whitney Rocketdyne

Ye Xu Oak Ridge National Laboratory

Matthew Zaluzec Ford Motor Company

Ji-Cheng Zhao The Ohio State University

Wayne Ziegler Army Research Lab

Frank Zok University of California, Santa Barbara

Alex Zunger University of Colorado, Boulder
Workshop Report: Building the Materials Innovation Infrastructure: 

Data and Standards

59

APPENDIX B: ACRONYMS 

API application programming interface

BSE Bethe-Salpeter equation 

CALPHAD calculation of phase diagrams 

DFT Density Functional Theory

DMRL data and model readiness levels

DOD U.S. Department of Defense

DOE U.S. Department of Energy

DOI Digital Object Identifiers®

LBNL Lawrence Berkeley National Laboratory

MGI Materials Genome Initiative

MII Materials Innovation Infrastructure

MIT Massachusetts Institute of Technology

NIST National Institute of Standards and Technology

QMC Quantum Monte Carlo

R&D research and development

TAA technical application area

TMF thermal mechanical fatigue