Economic Analysis of National Needs | Page 1

for Technology Infrastructure to Support the Materials Genome Initiative

Economic Analysis of National Needs 

for Technology Infrastructure to Support the 

Materials Genome Initiative

May 2018

RTI Project Number 0215231 

Prepared by 

Troy Scott

Amanda Walsh

Benjamin Anderson

Alan O’Connor

RTI International 

3040 E. Cornwallis Road 

Research Triangle Park, NC 

27709

Gregory Tassey

University of Washington

Seattle, WA 98195
Economic Analysis of National Needs | Page 2

for Technology Infrastructure to Support the Materials Genome Initiative

Analysis Brief

The MGI is a strategic effort spanning multiple federal 

agencies to promote a globally competitive U.S. manufacturing sector by addressing important gaps in the Materials 

Innovation Infrastructure1. Its aim is to enable U.S. companies 

to more rapidly and efficiently develop and deploy advanced 

materials, with applications ranging from consumer goods, to 

renewable energy generation and energy storage, to supercomputing and national defense.

The National Institute of Standards and Technology (NIST) is 

supporting the MGI through efforts to establish

• materials data-exchange and model-exchange protocols; 

• the means to ensure the quality of materials data and 

models; and 

• new methods, metrologies, and capabilities needed for 

accelerated materials development.

Additionally, through its integration of these activities, NIST 

is working to test and disseminate elements of an improved 

Materials Innovation Infrastructure to stakeholders in other 

national laboratories, universities, and U.S. industry.

This briefing provides key findings from economic analysis and interviews with more than 120 industry experts on 

their needs for new technological infrastructure supporting 

advanced materials innovation and the potential economic 

impacts of meeting those needs. We conclude that an 

improved Materials Innovation Infrastructure would 

deliver between $123 billion and $270 billion in value 

annually.

ABOUT THE MATERIALS GENOME INITIATIVE

Advanced materials are an increasingly essential—and 

increasingly complex—component of the manufacturing 

environment, forming the first tier of advanced manufacturing supply chains.2

 This complexity demands larger, more 

diversified, and integrated R&D resources, which are not 

easily defined and implemented.3

Recognizing this challenge, more industrialized nations are 

making larger and more sophisticated investments in new 

materials and their product applications, especially in materials R&D infrastructure.

1 To make this analysis tractable, the central concept of a Materials Innovation Infrastructure was reduced to the six industry needs summarized in Table 1.

2 Moskowitz, S. L. (2014). The advanced materials revolution: Technology and economic growth in the age of globalization. John Wiley & Sons, Inc., New York.

3 Tassey, G. (2016). The technology element model, path-dependent growth, and innovation policy. Economics of Innovation and New Technology, 25(6), 594-612.

$123 Billion per year. $270

The aim of the Materials Genome Initiative 

is to enable U.S. industry to develop and 

deploy advanced materials more quickly 

and efficiently. 

NIST is a key player in supporting the MGI approach and the development of a national Materials Innovation 

Infrastructure. This analysis presents estimates of potential impacts attributable to improved infrastructure of 

between $123 billion and $270 billion per year. 
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for Technology Infrastructure to Support the Materials Genome Initiative

Examples include the Industrial Technology Research 

Institute of Taiwan, the Electronics and Telecommunications 

Research Institute of South Korea, the Fraunhofer applied 

research institutes in Germany, and, in the United States, 

the National Network for Manufacturing Innovation 

(Manufacturing USA), Advanced Manufacturing Technology 

Consortia, National Nanotechnology Initiative, the MGI, and 

other initiatives.4

Launched in 2011 as a key enabling element of a strategy 

to spark domestic competitiveness in manufacturing in 

high-demand and emerging technology markets, the MGI is a 

“multi-stakeholder effort to develop infrastructure to accelerate advanced materials discovery and development in the 

United States . . . [and] leverage existing Federal investments 

through the use of computational capabilities, data management, and an integrated approach to materials science and 

engineering.” 5

Motivating the initiative are two ideas: first, that developing 

advanced materials is critical to addressing challenges in 

sectors like energy, supercomputing, national security, and 

healthcare; second, that accelerating the process of moving 

an advanced material from laboratory to market could significantly improve U.S. global competitiveness and ensure that 

the United States remains at the forefront of the advanced 

materials marketplace.

Released in 2014, the MGI strategic plan sets out four defining 

goals for the initiative: 6

• Culture change: MGI-aligned efforts will aim to improve 

knowledge flows and break down traditional silos in 

materials science and engineering, integrating the 

efforts of theorists and experimentalists, and promoting 

collaboration among academia, national and federal 

laboratories, and industry.

• Integration of experiments, computation, and theory: 

A defining feature of MGI-aligned efforts is an integrated, 

collaborative workflow that draws simultaneously from 

experiments, computation, and theory. The Materials 

Innovation Infrastructure envisioned by the MGI includes 

advanced simulation tools validated through experimental data, networks to share useful modeling and analysis 

code, and access to quantitative synthesis and characterization tools.

• Access to digital data: MGI-aligned efforts will expand 

access to validated data and tools generated by the 

materials community across the materials development 

continuum.

• Workforce development: To prepare the next generation of materials scientists and engineers to leverage a 

new Materials Innovation Infrastructure and apply the 

integrated, systems approach to materials innovation it 

enables, MGI-aligned efforts will support undergraduateand graduate-level curriculum development together 

with workforce development and training for professionals in the workplace.

Leading the MGI are the Department of Defense, Department 

of Energy, National Science Foundation, and NIST. Other 

agency partners in the MGI include the National Aeronautics 

and Space Administration; National Institutes of Health; and 

U.S. Geological Survey, Department of the Interior.7

4 See www.manufacturing.gov/programs.

5 National Science and Technology Council (NSTC). (June 2011). Materials Genome Initiative for Global Competitiveness.

6 National Science and Technology Council (NSTC). (December 2014). Materials Genome Initiative Strategic Plan.

7 See https://www.mgi.gov/partners.

The MGI “offers a unique opportunity 

for the United States to discover, 

develop, manufacture, and deploy 

advanced materials at least twice as 

fast as possible today, at a fraction of 

the cost” (NSTC, 2011).
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for Technology Infrastructure to Support the Materials Genome Initiative

Access to High-Quality Data

Nonproprietary experimental data, 

computational data, metadata, and 

software code

Collaborative Networks

Efficient means of sharing materials 

information (e.g., along a supply 

chain, among research collaborators)

Material Design Methods

Enabling application of a systems 

approach to materials development, 

from discovery and design all the way 

through to deployment

• Fundamental materials data

• Data standardization and curation

• Models underpinning accurate and 

repeatable material measurement

• Methods for capturing, characterizing, 

and sharing materials data in structured 

formats

• Communication standards and translators 

(“MT Connect for material measurement 

equipment”)

• Models, simulations, and metrologies for 

advanced materials design and means of 

integrating tools with one another.

• Machine learning tools

• More easily leverage prior research with 

less duplication of effort

• Enable greater reliance on more efficient 

computational approaches

• Multiply the value of every other element 

of a Materials Innovation Infrastructure

• Align academic and public-sector research 

to industry-relevant challenges

• Integrate experimental measurement and 

computational modeling to improve model 

fidelity and overall utility

• Realize network externalities

• Enable more targeted searches of design 

space for promising candidate materials

• Enable purposeful design of materials to 

meet specific performance requirements 

for targeted applications

• Target significant performance 

improvements with more-novel materials, 

as opposed to seeking smaller incremental 

improvements by refining known materials

• Enable co-design of new materials and 

new product applications

INDUSTRY NEED EXAMPLES OF INFRASTRUCTURE 

TECHNOLOGY TO ADDRESS NEED POTENTIAL IMPACTS

Table 1. Technology Infrastructure Needs for Advanced Materials Innovation

Production & Scale-Up

Model-based alternatives to expensive 

physical testing, trial and error–based 

approaches

Faster, cost-effective means of 

producing advanced materials at pilot 

and full scales

• Multiscale modeling frameworks 

(integrating macroscopic process models 

with microscopic materials simulation)

• Process technology platforms (e.g., cold 

sintering, additive manufacturing, roll-toroll printing, directed self-assembly)

• Reduce trial and error when scaling up 

(from lab scale to pilot scale, from pilot 

scale to production scale)

• Allow consideration of production-scale 

processes to be integrated into the initial 

design process

• Overcome the “Valley of Death” between 

lab scale and production scale: pilot-scale 

manufacturing services and facilities are 

underprovided by the market
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for Technology Infrastructure to Support the Materials Genome Initiative

INDUSTRY NEED EXAMPLES OF INFRASTRUCTURE 

TECHNOLOGY TO ADDRESS NEED POTENTIAL IMPACTS

Quality Assurance, Quality Control 

& Component Certification

Ability to model, predict, and control 

formation of defects

Ability to forecast manufacturing 

variation

Model Validation & Uncertainty 

Quantification

Basis for trust and acceptance of 

computational models

Basis for objective decision-making 

regarding reliance on computational 

analysis and simulation at a business 

level

• Performance metrics (benchmarks, 

reference data, testbeds to characterize 

performance of systems and 

components)

• Process control tools (test protocols, 

objective scientific and engineering data, 

reference databases)

• Generally accepted and easily applied 

methods for uncertainty quantification 

for both experimental and computational 

data

• Validation of analytical methods and 

procedures, emphasizing industrially 

relevant systems, comparing predicted 

and measured properties from multiple 

sources

• Reduce the cost of controlling and 

verifying the performance attributes 

of materials—and components and 

products embodying those materials

• Reduce the risk of large costs incurred 

if defects are not detected and lead to 

product failures in use (e.g., lithium-ion 

battery fires)

• Enhance the utility of computational 

approaches from an engineering 

perspective

• Enable rational decision-making 

regarding computational approaches 

from a business perspective

• Advance industry’s reliance on 

computational approaches in situations 

where they can save cost and add value 

Table 1. Technology Infrastructure Needs for Advanced Materials Innovation (continued)

INDUSTRY NEEDS ASSESSMENT 

Six identified areas of industry need provided the common 

basis for the more than 120 interviews that support this 

analysis:

• access to high-quality data;

• collaborative networks; 

• material design methods; 

• production and scale-up;

• quality assurance, quality control, and component 

certification; and

• model validation and uncertainty quantification (Figures 1 

and 2).

Access to high-quality data emerged as a linchpin of a 

Materials Innovation Infrastructure. Industry experts stressed 

the strong complementarity among the six areas of need and 

the consequent overlap among the types of infrastructure 

and potential impacts: efforts to address one need tend to 

increase the return on efforts to address the others. But the 

complementarity was not completely symmetrical: access to 

high-quality data was perceived to play a pivotal role, being a 

prerequisite for model validation and uncertainty quantification and for the productive application of machine learning, 

modeling and simulation, and other elements of an envisioned Materials Innovation Infrastructure. The upshot is that 

addressing industry’s need for high-quality, nonproprietary 

digital data can be expected to lower the barriers—faced by 

both the public and the private sectors—to addressing the 

other areas of need.
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for Technology Infrastructure to Support the Materials Genome Initiative

Figure 1. Interviewees’ Rating of Importance of Technology Infrastructure Needs

Note: Percentages shown reflect the distribution of ratings. Average ratings are given in parentheses below each area of industry need.

Figure 2. Interviewees’ Rating of Difficulty of Meeting Needs through Private Investment

Note: Percentages shown reflect the distribution of ratings. Average ratings are given in parentheses below each area of industry need.

43%

19%

45%

18%

34%

35%

34%

21%

27%

21%

32%

19%

20%

12%

16%

34%

43%

25%

30%

42%

37%

39%

41%

43%

39%

42%

10%

30%

12%

29%

17%

29%

18%

26%

27%

24%

5%

5%

7%

15%

8%

9%

5%

5%

9%

11%

9%

11%

12%

19%

3%

4%

5%

2%

2%

5%

9%

5%

3%

4%

Access to 

High-Quality Data 

(mean: 4.1)

Access to 

High-Quality Data 

(mean: 3.6)

Quality Assurance & 

Component Certification 

(mean: 4.1)

Quality Assurance & 

Component Certification 

(mean: 3.5)

Model Validation & 

Uncertainty Quantification 

(mean: 3.9)

Model Validation & 

Uncertainty Quantification 

(mean: 3.7)

Materials Design Methods 

(mean: 3.7)

Materials Design Methods 

(mean: 3.7)

Production & Scale-Up 

(mean: 3.8)

Production & Scale-Up 

(mean: 3.6)

Collaborative Networks 

(mean: 3.5)

Collaborative Networks 

(mean: 3.1)

5 - critically 4 - very 3 - moderately 2 - somewhat 1 - not important

5 - prohibitively 4 - very 3 - moderately 2 - somewhat 1 - not difficult
Economic Analysis of National Needs | Page 7

for Technology Infrastructure to Support the Materials Genome Initiative

Figure 4. Potential Impact on Time to Market (Years)

Note: U.S. manufacturers would be able to bring new materials to market faster with the benefit of improved infrastructure. Average 

time to market is estimated to be 6.6 years with improved infrastructure compared with 10.2 years in the current environment.

IMPACT ON RISK

Companies developing new materials face the risk that a 

research and development (R&D) project will fail to reach 

deployment and generate investment returns. We estimate 

that the total risk could be reduced by almost half with 

IMPACT ON TIME TO MARKET

We estimate that development of a new material takes on 

average more than 10 years and that an average acceleration 

improved infrastructure: for each new material deployed, 

only 5 R&D projects would need to enter the R&D pipeline at 

the discovery/design stage, down from an estimated 9.8 in 

the current environment (Figure 3). 

of 3.5 years could be possible with improved infrastructure 

(Figure 4).

Figure 3. Potential Impact on Risk

Note: With improved infrastructure, materials R&D projects are more likely to transition to successive stages. The number of projects that must enter the R&D 

pipeline at the discovery/design stage for each one that successfully reaches the final deployment stage improves from 9.8 to 5.0 (a 20% chance of deployment 

with improved infrastructure versus a 10% chance currently). The number of projects that must enter the development stage for every one that reaches the 

deployment stage improves from 2.9 to 2.1 (a 48% chance of deployment, conditional on reaching the development stage, with improved infrastructure versus a 

35% chance currently). R&D Projects in Stage per Deployment

10.0

9.0

8.0

7.0

6.0

5.0

4.0

3.0

2.0

1.0

0.0

0 2 4 6 8 10 12

2.9

2.1 1.4

1.3

1.0

9.8

5.0

Improved Infrastructure

Existing Infrastructure

3.5-year

acceleration

Discovery/Design

Improved

Infrastructure

Development Manufacturing Deployment

1.4

2.6 3.0

2.0 1.4 1.8

2.0 2.5

Discovery/Design Development Manufacturing Deployment
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for Technology Infrastructure to Support the Materials Genome Initiative

IMPACT ON RELATIVE COSTS PER PROJECT 

PER YEAR 

We estimate that improved infrastructure has the potential 

to reduce relative costs by an average of 25% in the discovery/design stage, 45% in the development stage, 48% in 

the manufacturing stage, and 28% in the deployment stage 

(Figure 5). 

Overall, estimated potential impacts of an improved Materials 

Innovation Infrastructure achieve a 71% improvement in 

R&D efficiency, worth an estimated $39 billion to $69 billion 

per year to U.S. companies that comprise the new materials 

supply chain (Table 2).8

IMPACTS ON PRODUCT QUALITY AND 

PERFORMANCE

The potential benefits of an improved Materials Innovation 

Infrastructure do not stop at raising R&D efficiency. The 

improved infrastructure could also enable companies to 

undertake R&D projects they would not otherwise have done, 

leverage that R&D to commercialize improved products and 

new product lines, and expand into new markets. Not only 

would the expected R&D cost be lower for a given R&D result, 

the anticipated R&D result would also be superior. 

One way in which this could happen is by enabling companies to incorporate new materials into the product design 

process earlier, so that new product applications take full 

advantage of new materials’ capabilities. Going even further, 

computational materials design methods, fed with high-quality, nonproprietary digital data, could enable co-design of 

new materials and new product applications.

We estimate the value of these additional benefits to be 

roughly 2 to 3 times the value of potential R&D efficiency 

impacts, or between 4% and 9% of the annual value added 

for the industries considered (i.e., 4% to 9% of these industries’ contributions to U.S. gross domestic product).

8 The estimated 71% improvement in R&D efficiency is the percentage reduction in average R&D investment cost per new material deployed. The range of $39 billion to $69 billion per 

year in potential impacts represents between 15% and 25% of R&D investment in the industries considered. 

Figure 5. Potential Impact on Relative R&D Cost per Project per Year

Note: Cost per project, per year, in the discovery/design stage in the current 

environment was normalized at 1.0. In the current environment, manufacturing and deployment stages are estimated to be, respectively, roughly 

four times and three times more cost-intensive than the development stage, 

which is, in turn, roughly four times more cost-intensive than the discovery/

design stage. Improved infrastructure is estimated to reduce relative costs by 

an average of 25% in the discovery/design stage, 45% in the development 

stage, 48% in the manufacturing stage, and 28% in the deployment stage.

Deployment

Manufacturing

Development

Discovery/Design

Beyond increasing R&D efficiency, 

improved infrastructure could 

also create new opportunities for 

companies to improve product 

quality and performance, expand 

product offerings, and enter new 

markets.

Relative Cost per Project per Year

35

30

25

20

15

10

5

0

Existing

Infrastructure

Improved

Infrastructure

11.95

16.52

8.59

8.55

2.16

3.90

1.00 0.75
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for Technology Infrastructure to Support the Materials Genome Initiative

TOTAL ECONOMIC IMPACTS

Altogether, the potential economic benefits of an improved 

Materials Innovation Infrastructure are estimated to be worth 

between $123 billion and $270 billion per year (Table 2).

Public investment is needed to create a Materials Innovation 

Infrastructure. To explain the difficulty of addressing infrastructure needs solely through private investment and so 

justify the need for public investment in Materials Innovation 

Infrastructure, industry experts emphasized the public-good 

content of this infrastructure and the multidisciplinarity 

required to develop it.

Nonproprietary data is an example of a public good. A 

repository of measured basic properties of nonproprietary 

materials from different materials classes would be valuable 

to industry, providing an essential step to trusting computational models. Yet companies have weak incentives to direct 

their experimental groups to generate this kind of basic data.

Another example: developing a general architecture and 

tools for model validation and uncertainty quantification 

requires a combination of statistical analytic and materials 

engineering expertise surpassing what is typically required 

by the business model of any one company. Even when the 

multidisciplinary expertise does reside within a company, 

companies have weak incentives to develop and disseminate 

general-purpose tools and methods because they are public 

goods with value that is difficult to capture in the market.

Although it is ultimately through private-sector R&D investments that the potential economic benefits of an improved 

Materials Innovation Infrastructure will be realized, public 

investment is needed to create the infrastructure, thereby 

creating opportunities for productive private-sector investment.

Public investment is needed to create the infrastructure, thereby 

spurring the private-sector investments that will realize the benefits.

Table 2. Potential Economic Impact Estimates (Millions of 2013 U.S. Dollars Per Year)

Note: Potential R&D efficiency impact estimates are based on interview-based estimated impacts to the R&D process, summarized in Figures 3, 4, and 5, combined 

with industry R&D expenditure data (National Science Foundation, 2016) and interview-based estimates of the fraction of that expenditure related to developing 

new materials. Estimates of the value of improved R&D outcomes were also interview-based. Confidence intervals were calculated based on the variability of industry experts’ responses to interview questions, using a bootstrap approach described in the report. The larger confidence interval for improved R&D outcomes (80% 

of the point estimate compared with 53% for R&D efficiency) reflects greater variability among experts’ opinions and therefore greater uncertainty in the estimate. 

Point estimates of R&D efficiency and improved R&D outcomes impacts add to the total (the difference of 1 is due to rounding error). Confidence intervals cannot 

be added because the sources of uncertainty for the two types of potential impact are different and not perfectly correlated; the probability that both estimates 

(R&D efficiency and improved R&D outcomes) fall outside their respective confidence intervals is lower than the probability that either one does so.

TYPE OF POTENTIAL IMPACT DESCRIPTION POINT ESTIMATE 95% CONFIDENCE INTERVAL

R&D Efficiency R&D cost savings 56,421 (38,846 to 68,836)

Improved R&D Outcomes Superior performance of products emerging from R&D 151,447 (82,515 to 203,036)

Total 207,869 (123,229 to 270,047)