April 2018 

Economic Analysis of National 

Needs for Technology 

Infrastructure to Support the 

Materials Genome Initiative 

Final Report 

Prepared for 

Technology Partnerships Office, Office of Innovation and Industry 

 National Institute of Standards and Technology 

U.S. Department of Commerce 

Gaithersburg, MD 20899 

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 

Economic Policy Research Center 

RTI Project Number 0215231 

_________________________________ 

RTI International is a registered trademark and a trade name of Research Triangle Institute. iii 

Acknowledgments 

Many contributed to the preparation of this report, and we wish 

to acknowledge in particular the private companies, industry 

associations, research institutes, universities, and government 

agencies whose experts contributed the data and many 

thoughtful insights underlying this analysis. 

For their stewardship of this assessment, many helpful 

discussions, and comments on the final report, we are grateful 

to Courtney Silverthorn, Kathleen McTigue, James Warren, 

Michael Walsh, Heather Evans, and Carelyn Campbell.

The authors are responsible for all remaining mistakes and 

misunderstandings. 
iv

Notice 

This document was prepared as an account of work sponsored 

by an agency of the United States government. Neither the 

United States government nor any agency thereof, nor any of 

their employees, makes any warranty, express or implied, or 

assumes any legal liability or responsibility for the accuracy, 

completeness, usefulness or any information, apparatus, 

product, or process disclosed, or represents that its use would 

not infringe privately owned rights. Reference herein to any 

specific commercial product, process, or service by trade name, 

trademark, manufacturer, or otherwise does not necessarily 

constitute or imply its endorsement, recommendation, or 

favoring by the United States government or any agency 

thereof. The views and opinions of authors expressed herein do 

not necessarily state or reflect those of the United States 

government or any agency thereof. 
v

Contents 

Section Page 

Acknowledgments iii

Notice iv

Executive Summary ES-1

 Introduction 1-1

 Materials Genome Initiative: Overview 2-1

2.1 Vision and Goals ...................................................... 2-1

2.2 Federal Agency Partners ........................................... 2-3

2.2.1 Department of Defense .................................. 2-3

2.2.2 Department of Energy .................................... 2-4

2.2.3 National Science Foundation ........................... 2-5

2.2.4 National Institute of Standards and 

Technology ................................................... 2-6

2.3 MGI in Action .......................................................... 2-7

2.4 Public Policy Rationale .............................................. 2-9

 Methods and Analysis Approach 3-1

3.1 Scoping Interviews and Interview Guide 

Development ........................................................... 3-1

3.2 Materials R&D Process Model ..................................... 3-2

3.3 Expert Informant Interviews ..................................... 3-5

3.4 Economic Impact Estimation ..................................... 3-9
vi

3.5 Calculating Confidence Intervals .............................. 3-18

 Industry Needs Assessment 4-1

4.1 Access to High-Quality Data ...................................... 4-6

4.2 Collaboration Networks ........................................... 4-10

4.3 Materials Design Methods ....................................... 4-12

4.4 Production and Scale-up ......................................... 4-14

4.5 Quality Assurance and Control and Component 

Certification .......................................................... 4-17

4.6 Model Validation and Verification and Uncertainty 

Quantification ........................................................ 4-20

 Economic Impact Assessment 5-1

5.1 Calibrated R&D Process Model ................................... 5-2

5.2 Potential Economic Impact Estimates ......................... 5-5

5.3 Industry and Materials Class Results ........................ 5-11

 Conclusions 6-1

References R-1

Appendix 

Interview Guide .............................................................. A-1

 
vii

Figures 

Number Page 

3-1. Materials R&D Process Stages ........................................... 3-3

3-2. Distribution of Interviewees by Company Size ..................... 3-5

3-3. Distribution of Interviewees by Companies’ R&D 

Employment ................................................................... 3-6

3-4. Representation of R&D Stages among Interviewees ............. 3-6

3-5. Multiple R&D Stages Represented by Interviewees .............. 3-7

3-6. Representation of Materials Classes among Interviewees ..... 3-7

3-7. Representation of Industries among Interviewees ............... 3-8

3-8. Annual R&D Expenditure by Industry (millions of U.S. 

dollars) .......................................................................... 3-9

4-1. Interviewees’ Rating of Importance of Technology 

Infrastructure Needs ........................................................ 4-4

4-2. Interviewees’ Rating of Difficulty of Meeting Needs 

through Private Investment .............................................. 4-4

4-3. Interviewees’ Rating of Importance of Access to HighQuality Data ................................................................... 4-9

4-4. Interviewees’ Rating of Difficulty of Addressing the Need 

for High-Quality Data through Private Investment ............. 4-10

4-5. Interviewees’ Rating of Importance of Collaborative 

Networks ..................................................................... 4-12

4-6. Interviewees’ Rating of Difficulty of Provisioning 

Collaborative Networks through Private Investment ........... 4-12

4-7. Interviewees’ Rating of Importance of Materials Design 

Methods ....................................................................... 4-14

4-8. Interviewees’ Rating of Difficulty of Provisioning 

Infrastructure for Materials Design Methods through

Private Investment ........................................................ 4-14

4-9. Interviewees’ Rating of Importance of Provisioning 

Infrastructure for Production and Scale-Up ....................... 4-16
viii

4-10. Interviewees’ Rating of Difficulty of Provisioning 

Infrastructure for Production and Scale-Up through 

Private Investment ........................................................ 4-17

4-11. Interviewees’ Rating of Importance of Quality 

Assurance/Control and Component Certification ................ 4-19

4-12. Interviewees’ Rating of Difficulty of Provisioning 

Infrastructure for Quality Assurance/Control and 

Component Certification through Private Investment ......... 4-20

4-13. Interviewees’ Rating of Importance of Model 

Validation/Verification and Uncertainty Quantification ........ 4-22

4-14. Interviewees’ Rating of Difficulty of Provisioning 

Infrastructure for Model Validation/Verification and 

Uncertainty Quantification through Private Investment ...... 4-22

5-1. Materials R&D Process Stages ........................................... 5-1

5-2. Potential Impact on Successful Transition Rates .................. 5-3

5-3. Potential Impact on Time to Market ................................... 5-4

5-4. Potential Impact on Relative R&D Cost per Project per 

Year .............................................................................. 5-5

5-5. Potential Impact on Out-of-Pocket and Capitalized R&D 

Cost per Deployment ....................................................... 5-6

5-6. Materials R&D Expenditure and Percentage Savings ............. 5-8

5-7. Number of Discovery/Design Projects per New Material 

Deployed by Industry .................................................... 5-12

5-8. Percentage Reduction in Number of Discovery/Design 

Projects by Industry ...................................................... 5-13

5-9. Total Duration of R&D Stages by Industry ........................ 5-14

5-10. Percentage Reduction in Total Duration of R&D Stages 

by Industry .................................................................. 5-15

5-11. Number of Discovery/Design Projects per New Material 

Deployed by Materials Class ........................................... 5-17

5-12. Percentage Reduction in Number of Discovery/Design 

Projects by Materials Class ............................................. 5-17

5-13. Total Duration of R&D Stages by Materials Class ............... 5-18

5-14. Percentage Reduction in Total Duration of R&D Stages 

by Materials Class ......................................................... 5-18
ix

Tables 

Number Page 

ES-2. Potential Economic Impact Estimates ............................... ES-7

3-1. Materials R&D Process Model Parameter Estimates ............ 3-11

3-2. Materials R&D Process Model Parameter Estimates ............ 3-13

3-3. Total Weights by Industry and R&D Process Model 

Parameter .................................................................... 3-15

3-4. Interviewees Responding by Industry and R&D Process 

Model Parameter ........................................................... 3-16

3-5. Converting to Dollar-Denominated R&D Costs, 

Transportation Equipment .............................................. 3-17

4-1. Technology Infrastructure Needs for Advanced Materials 

Innovation...................................................................... 4-2

5-1. Materials R&D Process Model Parameter Estimates: 

Actual Scenario ............................................................... 5-2

5-2. Materials R&D Process Model Parameter Estimates: 

Counterfactual Scenario ................................................... 5-3

5-3. R&D Expenditure and Savings by Industry .......................... 5-7

5-4. Potential Economic Impacts by Industry ........................... 5-11

5-5. R&D Risk and Duration, Baseline and Potential Impact, 

by Industry .................................................................. 5-15

5-6. R&D Risk and Duration, Baseline and Potential Impact, 

by Materials Class ......................................................... 5-19

6-1. Potential Economic Impact Estimates ................................. 6-1

ES-1 

Executive Summary 

The Materials Genome Initiative (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 Infrastructure. Its aim is to 

enable U.S. companies to more rapidly and efficiently develop 

and deploy advanced materials, with applications ranging from 

consumer goods like the Apple Watch, 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 dataexchange 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 report, commissioned by NIST, presents analysis of 

perspectives and opinions of U.S. manufacturers and other 

industry experts on their needs for new technological 

infrastructure supporting advanced materials innovation and 

the potential economic impacts of meeting those needs. 

Estimated Value of Potential Impacts. The report presents 

estimates of the potential economic benefit of an improved 

Materials Innovation Infrastructure of between $123 billion and 

$270 billion per year, based on structured interviews with more 

than 100 industry experts. 

Industry Needs Assessment. Six identified areas of industry 

need provided the common basis for the expert informant 

interviews (Table ES-1). All six areas of need were rated “very” 

or “critically” important by at least 60% of respondents. Five of 

the six needs were rated “very” or “prohibitively” difficult to 

address solely through private investment by at least 50% of 

respondents (Figures ES-1 and ES-2). 

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 

report presents estimates 

of potential impacts 

attributable to improved 

infrastructure of between 

$123 billion and $270 

billion per year. 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

ES-2

Table ES-1. Technology Infrastructure Needs for Advanced Materials Innovation 

Industry Need 

Examples of Infrastructure 

Technology to Address Need Potential Impacts 

Access to HighQuality Data  Fundamental materials data 

 Data standardization and 

curation 

 Models underpinning accurate 

and repeatable material 

measurement 

 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 

Nonproprietary 

experimental data, 

computational 

data, metadata, 

and software code

Collaborative 

Networks  Methods for capturing, 

characterizing, and sharing 

materials data in structured 

formats 

 Communication standards and 

translators (“MT Connect for 

material measurement 

equipment”) 

 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 

Efficient means of 

sharing materials 

information (e.g., 

along a supply 

chain, among 

research 

collaborators) 

Material Design 

Methods  Models, simulations, and 

metrologies for advanced 

materials design and means of 

integrating tools with one 

another. 

 Machine learning tools 

 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 

Enabling 

application of a 

systems approach 

to materials 

development, from 

discovery and 

design all the way 

through to 

deployment 

Production & 

Scale-Up  Multiscale modeling frameworks 

(integrating macroscopic process 

models with microscopic 

materials simulation) 

 Process technology platforms 

(e.g., cold sintering, additive 

manufacturing, roll-to-roll 

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 productionscale 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 

Model-based 

alternatives to 

expensive physical 

testing, trial and 

error–based 

approaches 

Faster, costeffective means of 

producing 

advanced materials 

at pilot and full 

scales 

(continued) 
Executive Summary 

ES-3

Table ES-1. Technology Infrastructure Needs for Advanced Materials Innovation 

(continued) 

Industry Need 

Examples of Infrastructure 

Technology to Address Need Potential Impacts 

Quality Assurance, 

Quality Control & 

Component 

Certification  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) 

 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) 

Ability to model, 

predict, and control 

formation of 

defects 

Ability to forecast 

manufacturing 

variation 

Model Validation & 

Uncertainty 

Quantification

 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 

 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 

Basis for trust and 

acceptance of 

computational 

models 

Basis for objective 

decision-making 

regarding reliance 

on computational 

analysis and 

simulation at a 

business level 

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. 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. 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

ES-4

Figure ES-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 ES-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. 

3% 3% 5% 9%

2%

9%

5% 7%

8% 5%

9%

12%

10% 12% 17% 18%

27%

16%

39% 34% 37% 41%

30%

43%

43%

45% 34% 27%

32%

20%

Access to HighQuality Data

(mean: 4.1)

Quality

Assurance &

Component

Certification

(mean: 4.1)

Model

Validation &

Uncertainty

Quantification

(mean: 3.9)

Materials

Design

Methods

(mean: 3.7)

Production &

Scale-Up

(mean: 3.8)

Collaborative

Networks

(mean: 3.5)

5 - critically

4 - very

3 - moderately

2 - somewhat

1 - not important

4% 4% 2%

5% 5%

11%

5% 15% 9% 5% 11%

19%

30% 29% 29% 26% 24%

34%

41% 35% 39% 43% 42%

25%

19%

18%

21% 21% 19%

12%

Access to HighQuality Data

(mean: 3.6)

Quality

Assurance &

Component

Certification

(mean: 3.5)

Model

Validation &

Uncertainty

Quantification

(mean: 3.7)

Materials

Design

Methods

(mean: 3.7)

Production &

Scale-Up

(mean: 3.6)

Collaborative

Networks

(mean: 3.1)

5 - prohibitively

4 - very

3 - moderately

2 - somewhat

1 - not difficult
Executive Summary 

ES-5

Potential 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 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 ES-3). 

Potential 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 of 3.5 years could be 

possible with improved infrastructure (Figure ES-4). 

Potential 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 ES-5). 

Figure ES-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). 

9.8

2.9

1.4

1.0

5.0

2.1

1.3

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

Discovery/Design Development Manufacturing Deployment

R&D Projects in Stage per Deployment

Existing

Infrastructure

Improved

Infrastructure

Estimated potential 

impacts include the 

elimination of almost half 

of R&D project attrition 

and a 35% acceleration 

of R&D projects to 

market. Overall, potential 

impacts achieve a 71% 

improvement in R&D 

efficiency, worth an 

estimated $39 billion to 

$69 billion per year to 

U.S. companies. 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

ES-6

Figure ES-4. Potential Impact on Time to Market 

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. 

Figure ES-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. 

Overall, estimated potential impacts of an improved Materials 

Innovation Infrastructure achieve a 71% improvement in R&D 

2.6

1.4

3.0

2.0

2.0

1.4

2.5

1.8

0 2 4 6 8 10

Existing

Infrastructure

Improved

Infrastructure

Years from Start of Discovery/Design Stage

Discovery/Design Development Manufacturing Deployment

3.5-year 

acceleration

1.00 0.75

3.90 2.16

16.52

8.55

11.95

8.59

0

5

10

15

20

25

30

35

Existing

Infrastructure

Improved

Infrastructure

Relative Cost per Project per Year

Deployment

Manufacturing

Development

Discovery/Design
Executive Summary 

ES-7

efficiency, worth an estimated $39 billion to $69 billion per year 

to U.S. companies that comprise the new materials supply 

chain (Table ES-2).1 

Potential 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. 

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

Type of Potential 

Impact Point Estimate 95% Confidence Interval 

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

Improved R&D 

Outcomes 151,447 (82,515, 203,036) 

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

Note: Potential R&D efficiency impact estimates are based on interview-based estimated impacts to the R&D 

process, summarized in Figures ES-3, ES-4, and ES-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. 

 

1

 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. 

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. 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

ES-8

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’ 

contribution to U.S. gross domestic product). 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 ES-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 privatesector investments that 

will realize the benefits. 
1-1

Introduction 

Material properties determine the form and function of all 

manufactured products. Superior materials therefore expand 

the frontier of possibility for new technologies, with applications 

ranging from consumer goods like the Apple Watch, to 

renewable energy generation and energy storage, to 

supercomputing and national defense. The industrial capability 

to most quickly and efficiently develop and deploy advanced 

materials is therefore critical to a globally competitive 

manufacturing sector. 

Launched in 2011 with the vision of enabling “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” (National Science and Technology Council, 2011, 

p. 5), the Materials Genome Initiative (MGI) is major effort 

spanning multiple U.S. government agencies to secure this 

critical capability for U.S. industry. 

The National Institute of Standards and Technology (NIST) is 

supporting the MGI through efforts to establish materials dataexchange 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 report, commissioned by NIST, presents analysis of the 

perspectives and opinions of U.S. manufacturers and other 

industry experts on their needs for new technological 

infrastructure supporting advanced materials innovation and 

the potential impacts of meeting those needs. It presents an 

assessment of industry needs for technology infrastructure—

The Materials Genome 

Initiative aims to promote 

U.S. global competitiveness in manufacturing by 

enabling U.S. companies 

to develop and deploy 

advanced materials more 

quickly and efficiently. 

This report presents an 

industry needs assessment 

and potential economic 

impact estimates, based 

on interviews with more 

than 100 industry experts. 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

1-2

including standards, measurement technology, and generalpurpose technology—and provides estimates of the potential 

impacts of an improved Materials Innovation Infrastructure 

aligned with the MGI. The report also discusses the economic 

rationale for public investment toward this goal, integrating 

economic principles with illustrative examples provided by 

industry experts. The report is organized as follows: 

Section 2 provides an overview of the MGI and the economic 

policy rationale for the public investment that supports it, 

particularly public investment in the technical elements of an 

improved Materials Innovation Infrastructure. 

Section 3 provides an overview of the analytic approach to the 

industry needs assessment and economic impact assessment. 

The section presents the six areas of industry need on which 

the assessment focuses and describes the process by which 

they were identified, based in part on preliminary scoping 

interviews with industry experts. It presents and explains the 

quantitative materials research and development (R&D) process 

model and describes how it was used in structured interviews to 

collect the data that supported estimation of the potential 

impacts of improved infrastructure. It describes the 

composition of the group of 121 industry experts interviewed. 

Section 4 describes the qualitative results of the industry needs 

assessment, summarizing relative importance ratings and 

difficulty ratings for each of the six identified needs and 

presenting commonly heard themes, insights, and examples 

that explain each need, its importance to industry, and the 

reasons public investment is required to address the need. 

Section 5 presents the quantitative results of the economic 

impact assessment, developed using the materials R&D process 

model described in Section 3, calibrated based on the 

perceptions and opinions of industry experts. Potential impact 

estimates are based on a comparison of two calibrations of the 

model: first reflecting the current environment, then reflecting 

an environment with an improved Materials Innovation 

Infrastructure. Therefore, we are able to offer, in addition to 

potential impact estimates, current benchmark estimates of the 

risks, time to market, and costs of developing new materials. 

Section 6 concludes the report with a summary of important 

results and policy implications.
2-1

Materials Genome 

Initiative: Overview

Advanced materials are an increasingly essential—and 

increasingly complex—component of the manufacturing

environment, forming the first tier of advanced manufacturing 

supply chains (Moskowitz, 2014). This complexity demands 

larger, more diversified, and integrated R&D resources, which 

are not easily defined and implemented (Tassey, 2013, 2016). 

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. 

Examples include the Industrial Technology Research Institute 

(ITRI) of Taiwan, the Electronics and Telecommunications 

Research Institute (ETRI) 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 (AMTech), National Nanotechnology Initiative, the 

MGI, and other initiatives.2

 2.1 VISION AND GOALS 

Launched in 2011 as a key enabling element of an 

administration-wide effort 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 

 

2

 See www.manufacturing.gov/programs. 

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” 

(National Science and 

Technology Council, 

2011, p. 5). 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

2-2

materials science and engineering” (National Science and 

Technology Council, 2011, p. 4). 

Goals and efforts of the MGI overlap with and complement 

those of other U.S. manufacturing initiatives. Advanced 

materials design is central to the activities of the Manufacturing 

USA institutes, including among others LIFT, focused on 

lightweight materials manufacturing; PowerAmerica, focused on 

wide-bandgap power electronics manufacturing; IACMI, focused 

on fiber-reinforced polymer composites manufacturing; and 

NextFlex, manufacturing thin, flexible electronic devices and 

sensors. The MGI is also linked to the National Nanotechnology 

Initiative by the Nanotechnology Knowledge Infrastructure 

Signature Initiative to develop “models, simulation tools, and 

databases that enable predictions of nanoscale material 

properties [and] implement predictive tools for materials 

production and manufacturing” (National Science and

Technology Council, 2014, p. 51). 

Motivating the initiative are the ideas that “development of 

advanced materials will fuel many of the emerging industries 

that will address challenges in energy, national security, 

healthcare” and that accelerating the process of moving an 

advanced material from laboratory to market “could 

significantly improve U.S. global competitiveness and ensure 

that the Nation remains at the forefront of the advanced 

materials marketplace” (National Science and Technology 

Council, 2011, p. 3). 

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

goals for the initiative: 

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” 

(National Science and Technology Council, 2014, p. 5). 

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” (National Science and 

Technology Council, 2014, p. 5). The Materials Innovation 

Infrastructure envisioned by the MGI includes “advanced 

The MGI envisions an 

integrated, systems 

approach to materials 

innovation, achieved 

through investment in a 

new Materials Innovation 

Infrastructure. 
Section 2 — Overview of Materials Innovation 

2-3

simulation tools validated through experimental data, networks 

to share useful modeling and analysis code, and access to 

quantitative synthesis and characterization tools” (National 

Science and Technology Council, 2014, p. 5). 

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” 

(National Science and Technology Council, 2014, p. 6). 

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 undergraduate- and graduate-level curriculum 

development together with workforce development and training 

for professionals in the workplace. 

 2.2 FEDERAL AGENCY PARTNERS 

The efforts of multiple federal agencies are coordinated by the 

Subcommittee on the Materials Genome Initiative (SMGI), 

established in 2012 under the National Science and Technology 

Council’s (NSTC) Committee on Technology. Each agency 

participating in the MGI is represented on SMGI. 

Leading the MGI are the Department of Defense, Department of 

Energy, National Science Foundation, and NIST. Others 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, among others 

(see https://www.mgi.gov/partners).3

 2.2.1 Department of Defense 

The Department of Defense (DOD) is a major consumer of 

advanced materials through its procurement of warfighting 

systems and invests in “multidisciplinary R&D efforts integrated 

along the full materials continuum from discovery through 

 

3

 These agencies are identified in the MGI Strategic Plan (National 

Science and Technology Council, 2014) as having a leading role in 

several milestone tasks. Comparison of agencies’ effort levels in 

terms of MGI-related expenditure is outside the scope of this report 

and would not be straightforward: “Like the President’s FY2015 and 

FY2016 budgets, the FY2017 budget does not include a table of 

agency funding for the MGI” (Sargent et al., 2017). 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

2-4

development, deployment, sustainment, and retirement of 

assets” (National Science and Technology Council, 2014, p. 42). 

Illustrative of MGI-aligned efforts by the DOD, the Air Force 

Research Laboratory Materials and Manufacturing Directorate’s 

Composites Performance team is developing tools to predict 

behavior and life of ceramic and polymer matrix composite 

materials with less reliance on experimentation: 

“Through in-house developed, physics-based modeling

techniques, the team determines how materials will behave 

under specific application environments—including extreme 

environments—that are relevant to current and emerging Air 

Force platforms. These models also aid in predicting material 

degradation and consequent damage initiation and growth over 

time. This predictive ability helps designers and maintainers 

develop better materials for highly demanding applications, 

define maintenance cycles more accurately, and evaluate 

effects of processing or in-service induced damage on system 

performance. As a result, aircraft designers will be able to avoid 

over-conservative or inefficient design, more accurately predict 

component life, and more effectively evaluate in-service issues 

that arise during system life” (Jordan, 2017, p. 1). 

The U.S. Naval Research Laboratory (NRL) Center for

Computational Materials Science recently announced the 

discovery of a candidate material for solid-state lasers and light 

emitting diodes (LEDs). The work, a collaboration between NRL 

researchers and an international team of physicists, identified 

nanocrystals made of cesium lead halide perovskites as having 

a unique property: rapid light emission in its lowest-energy 

state—i.e., a ground exciton state that is bright instead of dark. 

Applications include “more efficient lasers and LEDs with larger 

emission power at lower energy use, as well as faster switching 

for communication and sensors” (Parry, 2018, p. 1).

For more on DOD efforts aligned with the MGI, see 

www.mgi.gov/partners-involved/department-defense-dod. 

 2.2.2 Department of Energy 

MGI-aligned efforts by the Department of Energy (DOE) run the 

gamut from basic research to applied R&D. The Office of Basic 

Energy Sciences “supports fundamental research in materials 

sciences and engineering, chemistry, geosciences, and physical 

biosciences to understand, predict, and ultimately control 

MGI-aligned efforts at 

DOD include physicsbased modeling to predict 

the behavior and life of 

ceramic and polymer 

matrix composite 

materials. 
Section 2 — Overview of Materials Innovation 

2-5

matter and energy at the electronic, atomic, and molecular 

levels, including research to provide the foundations for new 

technologies relevant to DOE’s missions in energy, 

environment, and national security” (National Science and 

Technology Council, 2014, p. 43). Other DOE offices, like the 

Office of Energy Efficiency and Renewable Energy and Office of 

Fossil Energy, are bringing MGI approaches to their missionspecific R&D efforts, for example seeking lightweight structural 

materials to save energy by reducing the weight of vehicles and 

aircraft and high-performance functional materials to improve 

solar panel efficiency and battery energy density. 

In 2016, DOE launched two 4-year projects to leverage 

supercomputing capabilities at Oak Ridge National Laboratory 

(ORNL) and Lawrence Berkeley National Laboratory (LBNL) to 

develop software for the design of functional materials with 

applications in alternative and renewable energy. The two 

projects will “combine theory and software development with 

experimental validation, drawing on the resources of multiple 

DOE Office of Science User Facilities, including the Advanced 

Light Source at LBNL, the Advanced Photon Source at Argonne 

National Laboratory (ANL), the Spallation Neutron Source at 

ORNL and several of the five Nanoscience Research Centers 

across the DOE national laboratory complex” (Stark, 2016, p. 

1). 

Another DOE-led MGI showcase, the Energy Materials Network 

(EMN) “is taking a different approach to materials research and 

development (R&D) that aims to solve industry’s toughest clean 

energy materials challenges.” EMN is network of consortia, each 

convening “national labs, industry, and academia to focus on 

specific classes of materials aligned with industry's most 

pressing challenges related to materials for clean energy 

technologies.” 4

 

For more on DOE efforts aligned with the MGI, see 

www.mgi.gov/partners-involved/department-energy-doe. 

 2.2.3 National Science Foundation 

Through its program Designing Materials to Revolutionize and 

Engineer our Future (DMREF), the National Science Foundation 

(NSF) is supporting the MGI with extramural research funding. 

 

4

 See https://www.energy.gov/eere/energy-materials-network/energymaterials-network. 

MGI-aligned efforts at 

DOE include the 

application of 

supercomputing to 

develop powerful 

modeling and simulation 

tools to predict material 

properties and guide the 

design of next-generation 

functional materials. 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

2-6

DMREF grants fund research that “seeks to advance 

fundamental understanding of materials across length and 

timescales, thereby elucidating the effects of microstructure, 

surfaces, and coatings on the properties and performance of 

engineering materials” (National Science and Technology 

Council, 2014, p. 48). 

Examples of recent DMREF-funded research results include a 

fundamental discovery that provides a technology platform for 

next-generation communications and computing applications of 

oxide electronics materials (Meiller, 2018) and the discovery, 

based on predictive atomistic calculations performed at the 

National Energy Research Scientific Computing Center, that 

incorporating boron into the indium-gallium nitride material 

commonly used for solid-state lighting can improve energy 

efficiency (McAllister and Kioupakis, 2017). 

For more on NSF efforts aligned with the MGI, including 

information on recent DMREF awards, see 

www.nsf.gov/funding/pgm_summ.jsp?pims_id=505073. 

 2.2.4 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 necessary 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 industry. 

NIST is host to numerous projects focusing on materials 

research, including material-, process-, and application-specific 

efforts and cross-cutting initiatives. The NIST Material 

Measurement Laboratory is coordinating these activities in 

partnership with the NIST Information Technology Laboratory 

and with broad participation across the Institute. 

NIST’s support for MGI is consistent with its mission to promote 

U.S. innovation and industrial competitiveness. Through its 

established role in meeting U.S. industry’s needs for 

measurement and related infrastructure technologies and 

standards, NIST supports both conventional materials 

innovation and computational materials science and 

NIST is leveraging its 

expertise in generating, 

integrating, and curating 

critically evaluated data 

and models to establish 

essential materials data 

exchange protocols and 

the means to ensure the 

quality of materials data 

and models. 
Section 2 — Overview of Materials Innovation 

2-7

engineering. NIST, first as its predecessor agency the National 

Bureau of Standards, has been involved in leveraging highquality materials data for new material design since this aim 

first drew the combined attention of industry and federal 

agencies (Rumble and Westbrook, 1985). 

Prominent among NIST’s MGI-aligned efforts is its support for 

the Center for Hierarchical Materials Design, a consortium of 

university and national laboratories and industry partners 

focused on “developing the next generation of computational 

tools, databases and experimental techniques in order to 

enable the accelerated design of novel materials and their 

integration to industry” 

(http://chimad.northwestern.edu/about/). 

Complete details of NIST’s MGI-related efforts can be found on 

NIST’s website, http://mgi.nist.gov. 

 2.3 MGI IN ACTION 

Three short vignettes illustrate how the types of potential 

impacts discussed in this report are already happening in a 

limited way, as opportunities are realized by pioneers working 

in especially fruitful niches of industry, leveraging the Materials 

Innovation Infrastructure that exists today. The potential 

impacts presented in this report could be realized by improving 

and extending this infrastructure so that more companies can 

follow these examples. 

Virtual Aluminum Casting 

Ford Motor Company developed the Virtual Aluminum Castings 

(VAC) software, enabling its engineers to design engine 

components and simulate their production and testing on a 

computer over many iterations before touching any raw 

materials. Integrating commercial software packages and data 

with Ford’s proprietary data and original code, VAC tools 

“bridge the many key dimensional scales from the atomistic 

level to the component level” (Allison, Li, Wolverton, and Su, 

2006, p. 28). 

In 2006, when VAC was relatively new, Ford credited the 

software with a “15-25% reduction in the time it takes to 

develop a new cylinder head or block [and] millions of dollars in 

direct cost savings or cost avoidance” (Allison, Li, Wolverton, 

and Su, 2006, p. 35). 

MGI-aligned approaches 

enabled Ford Motor 

Company to accelerate 

the development of engine 

components by 15% to 

25%, saving millions of 

dollars over just a few 

years. 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

2-8

The development of VAC involved collaboration between Ford 

engineers and researchers at the University of Michigan, 

University of Illinois, Imperial College, Pennsylvania State 

University, and the University of Southern California and was 

“accomplished by a combination of theoretical, experimental, 

and computational technologies and . . . the development of a 

deep, fundamental understanding of dozens of separate 

phenomena” (Allison, Li, Wolverton, and Su, 2006, p. 28). 

Rapid Qualification of New Structural Alloys in Aerospace 

QuesTek Innovations LLC has applied a collection of

computational models to the design, development, and 

aerospace certification and flight qualification of advanced 

metal alloys, integrating different models and data sources into 

a suite of tools it has trademarked Materials by Design. 

QuesTek credits its use of these computational modeling tools 

with accelerating the development of several advanced alloys. 

(Sebastian and Olson, 2014). 

Ferrium M54 steel progressed from clean-sheet design to flightqualified, production hook shank parts for the U.S. Navy’s T-45 

aircraft in 9 years, compared with a 10- to 20-year timeframe 

typical for flight-critical components (Materials Innovation Case 

Study, 2016a). 

Directed Self-Assembly of Block Copolymers 

Exponential growth in computing power and data storage 

capacity over several decades has been driven by the 

increasing resolution of optical lithography, enabling the 

number of integrated circuits that fit on a microchip to roughly 

double every two years, following Moore’s Law. As conventional 

optical lithography reaches its physical limits, new technologies 

will be needed to sustain this rate of innovation. 

One leading candidate is directed self-assembly (DSA) of block 

copolymers (Laachi, Shykind, and Fredrickson, 2014; Laachi et 

al., 2015). The promise of this novel nanoscale patterning 

technique rests on decades of research in the MGI mode. Wellintegrated theory, computation, and experimentation by 

research teams at universities and in industry has provided the 

necessary knowledge base (de Pablo et al., 2014). Recently, 

“integration of computation and experiments between

researchers at the University of Chicago, AZ Electronic 

Materials, Tokyo Electron Ltd., and Imec has resulted in 

MGI-aligned approaches 

enabled QuesTek to 

accelerate the 

development of a new 

steel for Navy aircraft. 

Integrated theory, 

computation, and 

experimentation have 

unlocked the promise of 

block copolymers for 

semiconductor devices 

and other applications. 
Section 2 — Overview of Materials Innovation 

2-9

demonstration of the world’s first 300-mm fab compatible 

directed self-assembly (DSA) process line.” (de Pablo et al., 

2014, p. 112). 

 2.4 PUBLIC POLICY RATIONALE 

The public sector’s role in performing basic research is 

generally understood. Less well understood is the appropriate 

scope of the public sector’s involvement in the process by which 

fundamental knowledge is translated into commercial

technologies. Incentives for private-sector investment do not 

snap suddenly from inadequate (basic science) to adequate (all 

subsequent steps). 

Efficient translation of fundamental knowledge into commercial 

technology depends on a bulwark of technology elements, 

having varying degrees of public-good content, that together 

comprise a technical infrastructure supporting innovative 

activity in the private sector. As with basic, nonmarket-oriented 

scientific knowledge, the private sector has weak incentives to 

supply these quasi-public goods. 

The quasi-public-good elements of the technical infrastructure 

include proof-of-concept research resulting in the creation of 

“technology platforms” and “infratechnologies” like

measurement and test methods, scientific and engineering 

data, quality control techniques, and the functional and physical 

basis for interfaces between components of technology 

systems. Tassey (2008, 2013, 2016) defines these technology 

elements, explains why their public-good characteristics lead to 

significant private-sector underinvestment, and analyzes their 

critical role in an efficient innovation ecosystem. 

Technology platforms are precompetitive proof-of-concept 

technologies on which myriad commercial technologies can be 

based. Tassey (2008) offers the classic example of Bell Labs’ 

development of the transistor, based on the principles of solidstate physics. A related technology platform is the lightemitting diode (LED), the basis for numerous technologies 

commercialized over decades (Sanderson and Simons, 2014). 

Today, LEDs are ubiquitous, found in everything from flat 

screen televisions to household light bulbs. The scope of 

different applications of technology platforms is typically much 

broader than the market strategy of any one company, making 

it so that a company can only expect to capture a fraction of 

Technical Infrastructure, 

a bulwark of public-good 

technology elements, 

creates opportunities for 

productive private-sector 

R&D investment. 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

2-10

the value of developing the platform. This difficulty leads to 

underinvestment in technology platforms by the private sector, 

a market failure that public investment of the right sort can 

correct. 

Illustrative examples of infratechnologies include “research 

tools (measurement and test methods), scientific and 

engineering data, the technical basis for interface standards, 

quality control techniques” (Tassey, 2008, p. 616).

As technology platforms emerge and birth new commercial 

technologies, they typically require specialized 

infratechnologies. For example, the ever-smaller structures of 

next-generation semiconductor and digital storage devices are 

“challenging the resolution limits of current analytical and inline 

metrology tools” (Kline, Sunday, Windover, and Bunday, 2017, 

p. 014001). For the many solid-state lighting applications of the 

LED platform now ascendant, NIST is developing specialized 

metrology and calibration services and working with DOE and 

national and international standard-setting organizations to 

develop measurement standards to meet the industry’s 

emerging needs.5

Like the value created by investing in technology platforms, the 

value of developing infratechnologies is difficult for any single 

company to capture. Moreover, developing infratechnologies 

often requires highly specialized expertise different from that of 

the companies that could benefit from using these technologies. 

For these reasons, developing infratechnologies is typically 

outside the scope of these companies’ market strategies. 

The elements of an improved Materials Innovation 

Infrastructure aligned with the MGI are special examples of 

technology platforms and infratechnologies. 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.

 

5

 See www.nist.gov/programs-projects/solid-state-lighting-metrology. 
3-1

Methods and 

Analysis Approach

This section provides an overview of our methods and analytic 

approach. The section presents the six areas of industry need 

on which the assessment focuses and describes the process by 

which they were identified. It presents and explains the 

quantitative materials R&D process model and describes how 

structured interviews were used to collect the data needed to 

calibrate the model. It describes the composition of the group 

of 121 industry experts interviewed. It describes the methods 

used to develop economic impact estimates and confidence 

intervals from the calibrated R&D process model. 

 3.1 SCOPING INTERVIEWS AND INTERVIEW 

GUIDE DEVELOPMENT 

Review of peer-reviewed literature, white papers, issue briefs, 

and industry reports, supplemented by 18 scoping interviews 

with industry experts involved with MGI-aligned efforts, 

provided the foundation for this study. This initial step identified 

six broad areas of industry need: 

 access to high-quality data, nonproprietary 

experimental data, computational data, and software

code; 

 collaborative networks, efficient means of sharing 

materials information (e.g., along a supply chain or 

among research collaborators); 

 materials design methods, computational approaches 

providing shorter paths to better starting points for 

materials discovery and design; application of systems 

approach to materials; 

 production and scale-up methods, including modelbased and simulation-based alternatives to expensive 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

3-2

physical testing based on trial and error and faster, 

more cost-effective means of producing advanced 

materials at pilot scale and full scale; 

 quality assurance and control, and component 

certification methods, enabling improved capabilities 

to model, predict, and control the formation of defects 

and to forecast manufacturing variation; and 

 model validation and uncertainty quantification, 

providing a basis for trust and acceptance of 

computational models and objective decision-making 

regarding reliance on computational analysis and 

simulation at a business level. 

After characterizing these six areas of need, RTI developed an 

interview guide to gather two types of information: 

• perspectives of industry experts on the relative 

importance of these needs and the difficulty of 

addressing them through private investment, supported 

with explanation and examples based on these experts’ 

first-hand experience; 

• quantitative input on the potential impact of addressing 

the six areas of need with an improved Materials 

Innovation Infrastructure. 

The interview guide is provided in the appendix to this report. 

Importance and difficulty ratings, together with qualitative 

analysis of industry needs distilled from the perspectives 

industry experts shared, are presented in Section 4. 

Quantitative analysis of potential economic impacts is 

presented in Section 5. 

 3.2 MATERIALS R&D PROCESS MODEL 

For the purpose of quantifying the potential impacts an 

improved Materials Innovation Infrastructure could have on the 

materials R&D process, we employ a stylized model, breaking 

the materials R&D process into four successive stages: 

Discovery/Design, Development, Manufacturing, and 

Deployment (Figure 3-1). 

The Discovery and Design stage involves experimentation 

and modeling at the smallest scale, using coin-sized pieces of a 

metal or polymer, for example. The stage begins with a 

statement of intent to seek a new material for a given 

application or end use. It ends when researchers are satisfied 
Section 3 — Methods and Analysis Approach 

3-3

that they have identified candidate material compositions and 

processing methods that are promising enough to justify 

investing in the next stage, development. 

The Development stage involves pilot-scale synthesis, 

processing, and evaluation, starting with producing candidate 

materials in quantities sufficient for application-specific testing 

and ending when developers are satisfied that they have 

identified a candidate composition and processing approach 

that is ready to transition to production scale. 

The definition of pilot scale varies depending on the type of 

material and its intended use. For a new alloy, pilot scale might 

involve a 1,500-pound heat, compared with production-scale 

heats of multiple tons (Materials Innovation Case Study, 

2016a). In developing Gorilla Glass 3, Corning progressed 

immediately to a production-scale process in the development 

stage (Materials Innovation Case Study, 2016b). 

The Manufacturing stage involves production-scale product 

and process evaluation, starting with the first production-scale 

trial and ending with a finalized product, produced as it will be 

produced for use in commercial products. 

The Deployment stage is for integrating the new material into 

a commercial product—a new alloy into a production 

automobile engine component, a new glass into a mobile 

device, block copolymers into a microprocessor. The stage may 

involve application-specific tailoring of the material and 

development of supporting technologies needed to integrate the 

new material into the commercial product. 

Figure 3-1. Materials R&D Process Stages 

Note: The materials R&D process stages are based on the Quantitative Benchmark for Time to Market (QBTM) 

analytical framework, developed for NIST by Nexight Group and Energetics (2016). 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

3-4

To complete the materials R&D process model, we assumed 

that, for a given R&D project, each stage has a duration (the 

time from start to end) and a cost per year, and that there is a 

probability of transitioning from one stage to the next. A given 

R&D project will simply progress from one stage to the next or 

not, based on the technical success of the R&D performed and 

business decisions about its market potential and alignment 

with the company’s market strategies. Thinking of transition 

probabilities governing this process is a tractable way to model 

the risk a company faces of sinking investment into an R&D 

project that may never bring in revenue. 

RTI made two key simplifying assumptions. First, projects that 

begin an R&D stage were assumed to complete that stage. 

Therefore, the probability of deployment, conditional on 

reaching (starting) the deployment stage, is 100%. Second, we 

assumed R&D stages do not overlap. 

Departing from the first assumption would have complicated 

the modeling (if a project begins a stage but does not complete 

it, how much time does it spend in the stage before ending?). 

Although not strictly true, the assumption is fairly realistic 

because of the “gated” approach taken by many companies: a 

stage is green-lighted, completed, its results evaluated, and a 

determination made about whether to continue into the next 

stage. In reality, there are more than four such gates, which 

divide these four broad stages with intermediate gates. 

Departing from either assumption would have required placing 

additional burden on interviewees by asking them to provide 

more detailed descriptions of the R&D process. Lessening this 

burden enabled us to focus more attention during interviews on 

qualitative discussions of the identified needs. 

The model does not represent the entire technology lifecycle, 

ending with the first use of a new material in a commercial 

application. The process of market diffusion, of a new material 

finding its way into new applications and expanding its share of 

the market in those applications, is not modeled here. Although 

this process is important to consider for innovation policy, 

including public investments in technical infrastructure, it is less 

directly related to the vision of the MGI. Concentrating on the 

R&D process from discovery and design through deployment 

allowed us to focus resources on the most important issues for 

technical infrastructure and policy related to the MGI. 
Section 3 — Methods and Analysis Approach 

3-5

 3.3 EXPERT INFORMANT INTERVIEWS 

Structured interviews were conducted with 121 experts, 116 of 

whom worked in industry; of the other 5, 3 had worked for 

companies and now consulted, and 2 worked in government 

laboratories doing applied materials research in collaboration 

with industry partners. 

The distribution of interviewees by company size is shown in 

Figure 3-2, together with the distribution of U.S. manufacturing 

companies’ R&D expenditure. With the exception of companies 

of less than 50 employees, which are overrepresented among 

our interviewees, and to a lesser extent companies with 5,000 

to 24,999 employees, which are underrepresented, the 

distributions are similar. Roughly half of interviewees 

represented companies of fewer than 500 employees, which are 

98% of all U.S. manufacturing companies (U.S. Census, 2015 

County Business Patterns). 

Companies represented by interviewees were, on average, 

more R&D-focused than typical U.S. manufacturing companies. 

Based on the R&D employment of these companies, reported 

by interviewees (Figure 3-3), our interviewees represented 

companies with a total R&D employment of between 34,000 

Figure 3-2. Distribution of Interviewees by Company Size 

Note: Distribution of U.S. manufacturing companies’ R&D expenditure, which is offered for comparison, comes from 

NSF (2016, Table 21). With the exception of companies with less than 50 employees, which are overrepresented 

among our interviewees, and to a lesser extent companies with 5,000 to 24,999 employees, which are 

underrepresented, the distribution of companies represented by experts with whom we spoke is similar to the 

distribution of U.S. manufacturing companies’ R&D expenditure. 

23%

7%

10%

13% 14%

33%

4%

6% 7%

18%

30%

35%

0%

5%

10%

15%

20%

25%

30%

35%

40%

Less than 50 50 to 249 250 to 999 1,000 to 4,999 5,000 to 24,999 25,000 or more

Number of Employees

Percentage of Companies Represented by Interviewees

Percentage of R&D Expenditure by U.S. Manufacturing Companies
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

3-6

and 80,000, or between 4% and 9% of U.S. manufacturing 

R&D employment (National Science Foundation, 2016). By 

comparison, our industry experts represented 0.05% of U.S. 

manufacturing companies and 1.4% of U.S. manufacturing 

companies with 500 or more employees. 

The industry experts who shared their perspectives and 

opinions for this study come from both experimental and 

computational backgrounds, and their expertise covers the four 

stages of the materials R&D process model: discovery/design, 

development, manufacturing, and deployment (Figures 3-4 and 

3-5). Many of these industry experts were also familiar with the 

Figure 3-3. Distribution of Interviewees by Companies’ R&D Employment 

Note: This graph is based on interviewees’ responses to question V.1 in the interview guide, which is provided in 

the appendix. 

Figure 3-4. Representation of R&D Stages among Interviewees 

Note: Each interviewee may have expertise in multiple stages. Therefore, figures sum to more than the unique 

number of interviewees, which is 121. 

18%

25%

23%

14%

16%

4%

0%

5%

10%

15%

20%

25%

30%

Less than 10 10 to 24 25 to 99 100 to 999 1,000 to 2,499 2,500 or more

Number of Employees Engaged in R&D

47

50

52

35

7

6

4

1

44

47

47

40

0 20 40 60 80 100 120

Discovery/Design

Development

Manufacturing

Deployment

Experimental Computational Both
Section 3 — Methods and Analysis Approach 

3-7

academic research setting, having completed advanced 

degrees, worked in academia, or worked with university 

researchers serving in consulting roles on commercial projects. 

Interviewees’ expertise covers a range of materials classes, 

with the greatest representation for lightweight and structural 

materials (predominantly alloys), ceramics, polymers, and 

polymer composites (Figure 3-6). 

Figure 3-5. Multiple R&D Stages Represented by Interviewees 

Note: The expertise of three non-industry respondents was in fundamental research. 

Figure 3-6. Representation of Materials Classes among Interviewees 

Note: Each interviewee may have expertise in multiple materials classes. Therefore, figures sum to more than the 

unique number of interviewees, which is 121. 

65

23

21

9

3

0 10 20 30 40 50 60 70

4 stages

3 stages

2 stages

1 stage

Research Only

23

29

21

18

20

14

7

9

4

6

3

1

27

6

4

2

3

1

3

3

0

3

0

0

0

3

28

21

21

15

10

9

8

7

7

6

4

3

24

0 10 20 30 40 50 60

Lightweight and Structural

Ceramics

Polymers

Polymer Composites

Electronic and Photonic Materials

Energy Storage

Catalysts

Biomaterials

Photovoltaic

Organic Electronic

Caloric Materials

Correlated Materials

Other

Experimental Computational Both
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

3-8

Interviewees represented companies in or developing materials 

for 10 manufacturing industries and one relevant service 

industry: physics, engineering, and life sciences R&D 

(Figure 3-7). Most manufacturing R&D expenditures are 

concentrated in transportation equipment, which includes 

aerospace, and in computer and electronic products, which 

includes semiconductors (Figure 3-8); out of the 121 industry 

experts interviewed, 51 had expertise in at least one of these 

two industries. 

Figure 3-7. Representation of Industries among Interviewees 

Note: Each interviewee may develop materials for multiple industries. Therefore, figures sum to more than the 

unique number of interviewees, which is 121. 

18

12

9

9

12

9

9

10

8

5

6

4

5

5

3

1

3

3

1

1

1

2

12

14

14

11

10

8

7

7

7

8

4

0 5 10 15 20 25 30 35

Fabricated Metal Products (332)

Transportation Equipment (336)

Computer and Electronic Products (334)

Machinery (333)

Primary Metals (331)

Chemical (325)

Electrical Equipment and Components (335)

Misc., incl. Medical Devices (339)

Petroleum and Coal Products (324)

Phys, Engin, and Life Sci (excl. Biotech) R&D (541712)

Plastics and Rubber Products (326)

Experimental Computational Both
Section 3 — Methods and Analysis Approach 

3-9

Figure 3-8. Annual R&D Expenditure by Industry (millions of U.S. dollars) 

Note: 2013 domestic R&D comes from NSF (2016, Table 23). North American Industry Classification System

(NAICS) identifiers are given in parentheses. R&D reported for the chemical industry (325) excludes 

pharmaceuticals and medicines (3254), which account for $52 billion in R&D. 

 3.4 ECONOMIC IMPACT ESTIMATION 

RTI estimated the potential economic impacts of an improved 

infrastructure addressing the identified needs of industry by 

calibrating the materials R&D process model (Section 3.2) in 

two scenarios: first, in the current environment with existing 

infrastructure (actual scenario) and then in a hypothetical 

environment with improved infrastructure (counterfactual 

scenario). Comparison of the models in the two scenarios 

yielded estimates of the relative reduction in R&D cost, which 

was then applied to annual R&D expenditure. 

As part of the process of developing potential impact estimates, 

RTI developed benchmark estimates of the time, risk, and 

relative costs of developing a new material given the 

infrastructure that exists today. These benchmark estimates 

are presented with the impact estimates in Section 5. 

The approach to cost estimation explained below is adapted 

from one that is well established for estimating the costs of 

developing pharmaceuticals (DiMasi et al., 2003, 2016) and 

that has been applied to estimate potential impacts of improved 

infrastructure (Gallaher et al., 2007; Scott et al., 2014). 

2,212

45,972

67,205

12,650

624

9,238

4,136

13,509

242

8,910

3,650

0 10,000 20,000 30,000 40,000 50,000 60,000 70,000

Fabricated Metal Products (332)

Transportation Equipment (336)

Computer and Electronic Products (334)

Machinery (333)

Primary Metals (331)

Chemical (325)

Electrical Equipment and Components (335)

Misc., incl. Medical Devices (339)

Petroleum and Coal Products (324)

Phys, engin, and life sci (excl. biotech) R&D (541712)

Plastics and Rubber Products (326)
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

3-10

Defining Costs. The cost to be estimated is the economic cost 

of successfully deploying a new material, including the cost of 

failures (candidate materials that go through some R&D stages 

but are never deployed) and the cost of capital. The cost of 

capital reflects the opportunity cost of financial capital, which is 

a significant part of the true economic cost of R&D, regardless 

of how that R&D is resourced.6

In the model, R&D costs are capitalized from the date they are 

incurred until the end of the deployment stage, when a new 

material is first used in a commercial product application. This 

is typically the point at which the stream of revenues 

attributable to the R&D would begin, and it is a useful 

benchmark for calculating the real cost of the R&D. For a 

company to want to invest in R&D, the expected value of the 

stream of future returns, discounted back to this point, must be 

greater than the expected cost of the R&D, capitalized to this 

point. 

Consider the choice between investing, say, $100,000 in either 

an early-stage R&D project, to begin 10 years before the point 

of deployment, or in a later-stage R&D project, for a material 

that is only 1 year from deployment. Intuitively, it is more 

difficult to justify the investment in the early-stage R&D 

project, because it must generate a greater expected return to 

justify the longer wait.7

 

This feature of the model is important; omitting the opportunity 

cost of capital from the model would neglect this important 

reality and distort the analysis, making impacts on earlier 

stages of R&D appear less important than they really are. 

Stepping Through the Model. The model is presented with 

an extended example, using the materials R&D process model 

parameters obtained from interviews, which are presented in 

Table 3-1. The relative cost per year, per project, in the 

 

6

 Not only must outside investors or lenders receive an acceptable rate 

of return when R&D is financed, there is also the need to provide an 

analogous return on a company’s internal resources (as when 

paying for R&D with retained earnings) so that a company’s 

shareholders are content with the resources being used for R&D 

instead of being paid out as dividends. 

7

 Assuming an 8% cost of capital, the early-stage project would have 

to generate twice the return of the project that is only 1 year out: 

The long-term project would have to generate at least 

$100,000(1.0810), or $215,892.5, while the other would need to 

generate only $108,000 to be considered worthwhile. 
Section 3 — Methods and Analysis Approach 

3-11

discovery and design stage is normalized at 1. Heuristically, it 

may be convenient to think of that cost as $1, but within the 

model the cost is unitless. The method of conversion to dollars 

will be explained in a subsequent step. 

To explain the model, let us start with the final stage, 

deployment, and work backward. The average cost per project 

per year in the deployment stage is 11.9, and the deployment 

stage lasts an average of 2.5 years (Table 3-1). It was 

assumed that every project that starts the deployment stage 

completes it. That is, every new material that is made available 

as a commercial product (defining the start of the deployment 

stage in the model) will be used in a commercial product 

application (defining the end of the deployment stage). 

Under these assumptions, the contribution of the deployment 

stage to total out-of-pocket cost is the product of 11.9 per year 

and 2.5 years, or 29.8. Recall, this relative cost is unitless; it 

would be $29.8 if the cost of one project for one year in the 

discovery and design stage were $1. 

The calculation of capitalized cost assumed that out-of-pocket 

costs are incurred at a constant rate of 11.9 per year, and 

these costs are capitalized to the end of the deployment stage. 

The capitalized cost is therefore given by the following integral, 

Table 3-1. Materials R&D Process Model Parameter Estimates 

Model 

Parameter 

R&D Stage 

Discovery & 

Design Development Manufacturing Deployment 

Relative cost 

per year 

1 3.9 16.5 11.9 

Probability of 

advancing to 

next stage 

29% 48% 72% N/A 

Duration 

(years) 

2.6 3.0 2.0 2.5 

Note: The relative cost per year in the discovery and design stage is normalized at 1. For simplicity, projects that 

begin a stage were assumed to complete that stage. These numbers reflect the average responses from all 

interviews. For a response related to a given R&D stage to be included in these averages, the respondent must 

have reported expertise in the R&D stage. 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

3-12

where e is the base to the natural logarithms and r is the real 

cost of capital, as a continuously compounded annual rate:8

11.9  

	 
.



= 11.9 


. − ⁄. 

We assumed an 8% cost of capital and converted this to a 

continuously compounded annual rate by letting  = ln1.08 =

0.077. Feeding in this value for r, the contribution of the 

deployment stage to capitalized cost is 32.8. 

Out-of-pocket and capitalized costs can be calculated in a 

similar way for the manufacturing stage. The difference is that 

not every project that goes through the manufacturing stage 

will go through the deployment stage; interview data indicate 

that, on average, only 72.4% of projects will advance. 

Therefore, 1.38 projects (the reciprocal of 72.4%) must go 

through the manufacturing stage for every new material that is 

successfully deployed. The out-of-pocket cost of the 

manufacturing stage is therefore 2.016.5⁄0.724 = 46.4. 

The capitalized cost is given by the following integral, where e

is the base to the natural logarithms and r is the real cost of 

capital, as a continuously compounded annual rate, and p is the 

probability of deployment, conditional on reaching the 

manufacturing stage: 

16.5  

	 
.
.


.  0.724 ⁄ = 16.5 

. − 
.⁄0.724 

Still assuming an 8% cost of capital and converting to a 

continuously compounded annual rate by letting  = ln1.08 =

0.077, the contribution of the manufacturing stage to capitalized 

cost is 60.8. 

In general, the contribution of each stage to the out-of-pocket 

cost per new material deployed is given by 	!" − 	#$%⁄&, 

where is the cost per year per project in that stage; 	!" is 

the time, in years, from the start of the stage to the end of the 

deployment stage; 	#$% is the time, in years, from the end of 

the stage to the end of the deployment stage; and & is the 

probability of deployment, conditional on reaching the stage. 

 

8

 Continuous compounding allows tractable calculations with costs that 

accrue continuously over the durations of R&D stages; it does not 

result in larger estimates of capital costs because the cost of capital 

is adjusted: an annually compounded 8% rate is equivalent to a 

continuously compounded rate of ln(1.08), or 7.7%, because 1.08t

= eln(1.08)t

. 
Section 3 — Methods and Analysis Approach 

3-13

The contribution of each stage to the capitalized cost per new 

material deployed, capitalized to the end of the deployment 

stage, is given by this integral:

  

	 '()*(

+,-

. = & 

'()*( − +/-⁄&. 

Table 3-2 summarizes these parameters for each R&D stage, 

derived from the parameters given in Table 3-1, and gives the 

out-of-pocket and capitalized costs for each stage. Note that 

the probability of deployment, conditional on reaching a given 

stage, is given by the product of the transition probabilities for 

that and all subsequent stages. For instance, the probability of 

deployment, conditional on reaching development, is 48% 

times 72%. 

Table 3-2. Materials R&D Process Model Parameter Estimates 

Model 

Parameter Definition 

R&D Stage 

Discovery & 

Design 

Development Manufacturing Deployment 

0 Cost per year per 

project in that 

stage 

1 3.9 16.5 11.9 

121341 Time, in years, 

from the start of 

the stage to the 

end of the 

deployment stage 

10.2 7.5 4.5 2.5 

1567 Time, in years, 

from the end of 

the stage to the 

end of the 

deployment stage 

7.5 4.5 2.5 0 

8 Probability of 

deployment, 

conditional on 

reaching the 

stage 

10% 35% 72% 100%

Out-of-pocket cost 25.9 33.3 46.4 29.8 

Capitalized cost 51.1 53.0 60.8 32.8 

Note: The cost per year in the discovery and design stage is normalized at 1. As a result, out-of-pocket and 

capitalized costs are given in relative terms, as a multiple of the cost per year for a project in the discovery and 

design stage. For example, the total out-of-pocket cost for the manufacturing stage is 48.5 times the cost of a 

year in the discovery and design stage. For simplicity, projects that begin a stage were assumed to complete 

that stage. Therefore, the probability of deployment, conditional on reaching (starting) the deployment stage, is 

100%. A further simplifying assumption is that the R&D stages do not overlap. In reality, the start time of one 

stage need not be the same as the end time of the preceding stage. Departing from this assumption does not 

complicate the cost calculation but would have required placing additional burden on interviewees by asking 

them to provide more detailed descriptions of the R&D process. 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

3-14

Several details of the approach to impact estimation are worth 

noting: 

Aggregating Results by Industry. The R&D process model 

was calibrated separately for each industry for which R&D 

expenditures are reported by NSF (2016). 

The calibrations of the model for a given industry, under actual 

and counterfactual scenarios, are based on the responses of 

interviewees whose companies develop materials in or for that 

industry, based on question V.1 in the interview guide 

(provided in the appendix). Interviewees were allowed to 

indicate multiple industries. The responses of an interviewee 

who indicated a total of n industries receive a weight of 1/n in 

the calibration of each of those industries, thus the responses 

of each interviewee receive a total weight of 1 across all 

industries in the impact assessment. 

Interviewees were not required to answer every question 

applied to the calibration of the R&D process model in the 

actual and counterfactual scenarios (Tables 2 and 3 in the 

interview guide, provided in the appendix), and responses 

applicable to a given R&D stage were counted only if the 

respondent indicated experience in that stage, based on 

question I.1 in the interview guide. Tables 3-3 and 3-4 show 

the total weights of responses and the number of individuals for 

each question (R&D process model parameter) and for each 

industry. 

For each industry, actual and counterfactual costs, both out-ofpocket and capitalized, were calculated for each R&D stage; 

that is, the relative cost of each stage, as a multiple of 1 year 

of out-of-pocket costs in the discovery/design stage. These 

relative costs were then converted to dollar-denominated costs 

according to the following two steps: 

Step 1. Industry R&D Fraction. A dollar-denominated R&D 

discount factor was determined for each industry based on the 

fraction of R&D in each industry interviewees believed was 

focused on developing and applying new materials, based on 

question V.5 in the interview guide. Interviewees’ responses to 

question V.5 were combined for each industry as a weighted 

average, in the same way that the R&D process model

parameter responses were combined. 
Section 3 — Methods and Analysis Approach 

3-15

Table 3-3. Total Weights by Industry and R&D Process Model Parameter 

Actual Industry 

Dev 

R/C 

Mfctr 

R/C 

Deplo 

R/C 

D/D 

Prob 

Dev 

Prob 

Mfctr 

Prob 

D/D 

Dur 

Dev 

Dur 

Mfctr 

Dur 

Deplo

Dur 

Chemical (325) 7.9 5.6 4.4 8.9 7.9 5.6 8.9 8.9 5.6 4.4 

Primary Metal (331) 11.3 11.9 9.4 10.4 10.6 12.7 11.4 11.8 12.9 10.4 

Machinery (333) 8.2 7.7 5.4 5.6 7.0 7.5 6.6 8.2 8.7 5.2 

Electrical Equip. (335) 3.9 3.4 3.0 4.1 4.9 3.4 4.1 4.9 4.4 3.0 

Trnsport. Equip. (336) 12.2 12.3 9.2 11.8 12.0 12.1 11.8 12.2 12.3 8.6 

Petro/Coal (324) 4.7 4.5 4.3 5.7 4.7 4.5 5.7 4.7 4.5 4.3 

Plastics (326) 2.9 2.7 2.2 3.3 2.9 2.7 3.3 2.9 2.7 2.2 

Fabricated Metal (332) 8.7 9.3 5.7 8.9 9.2 9.3 7.9 9.2 8.3 4.9 

Computer/Electron (334) 6.5 6.0 3.7 8.2 7.5 6.0 8.2 7.5 6.0 4.7 

Misc. (339) 6.4 6.2 3.0 6.5 6.2 6.0 6.5 6.4 6.2 1.8

R&D Services (541712) 3.4 3.4 2.7 3.5 3.2 3.2 3.5 3.4 2.4 1.5 

Total 76 73 53 77 76 73 78 80 74 51 

Counterfactual 

Industry 

D/D 

R/C 

Dev 

R/C 

Mfctr 

R/C 

Deplo 

R/C 

D/D 

Prob 

Dev 

Prob 

Mfctr 

Prob 

D/D 

Dur 

Dev 

Dur 

Mfctr 

Dur 

Deplo 

Dur 

Chemical (325) 7.9 6.9 4.6 3.4 7.9 7.9 5.6 8.9 8.9 5.6 4.4 

Prim. Met. (331) 10.5 9.7 10.4 8.4 10.0 10.2 11.4 10.0 11.2 11.4 9.4 

Machinery (333) 4.5 5.8 5.5 3.7 4.5 5.5 6.2 4.5 5.8 6.5 3.7 

Elec. Equip. (335) 2.5 3.3 2.9 2.5 3.5 4.3 2.9 3.5 4.3 3.9 2.5 

Trnsp. Eqp. (336) 10.1 10.5 11.8 8.6 11.1 11.5 11.8 11.4 11.5 11.8 8.6 

Petro/Coal (324) 4.1 3.6 3.5 3.3 4.1 2.2 2.1 4.1 3.6 3.5 3.3 

Plastics (326) 2.6 2.8 2.7 2.2 2.6 2.8 2.7 2.6 2.8 2.7 2.2 

Fab. Metal (332) 6.5 6.8 7.5 4.9 6.0 5.9 6.2 6.0 7.3 7.5 4.9 

Comp. Elec. (334) 6.7 5.9 5.5 3.2 6.7 5.9 5.5 7.1 5.9 5.5 4.2 

Misc. (339) 6.4 5.6 5.5 2.3 6.4 5.6 5.5 6.4 5.6 5.5 1.3 

R&D (541712) 3.2 3.2 2.2 1.5 3.2 3.2 2.2 3.5 3.2 2.2 1.5 

Total 65 64 62 44 66 65 62 68 70 66 46 

Note: Interviewees were allowed to indicate multiple industries. The responses of an interviewee who indicated a 

total of n industries receives a weight of 1/n in the calibration of each of those industries. Numbers in this table 

reflect the total of these weights in each industry for interviewees answering each question. The responses of 

each interviewee received a total weight of 1 across all industries in the impact assessment; therefore, totals in 

this table match totals in Table 3-4. R&D stages: D/D, discovery/design; Dev, development; Mfctr, 

manufacturing; Deplo, deployment. Model parameters: R/C, relative cost; Prob, probability of advancing to next 

stage; Dur, duration. These parameters correspond to Table 2 (Actual Scenario) and Table 3 (Counterfactual 

Scenario) in the interview guide (provided in the appendix). Industries: 325, Chemical, excluding 

pharmaceuticals and medicines; 331, Primary metals; 333, Machinery; 335, Electrical Equipment, Appliances, 

and Components; 336, Transportation Equipment, including aerospace; 324, Petroleum and Coal Products; 326, 

Plastics and Rubber Products; 332, Fabricated Metal Products; 334, Computer and Electronic Products, including 

semiconductor and other electronic components; 339, Miscellaneous Manufacturing, including medical devices; 

541712, Physical, Engineering, and Life Sciences (except biotechnology) R&D. 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

3-16

Table 3-4. Interviewees Responding by Industry and R&D Process Model Parameter 

Actual Industry 

Dev 

R/C 

Mfctr 

R/C 

Deplo 

R/C 

D/D 

Prob 

Dev 

Prob 

Mfctr 

Prob 

D/D 

Dur 

Dev 

Dur 

Mfctr 

Dur 

Deplo

Dur 

Chemical (325) 14 10 8 15 14 10 15 15 10 8

Primary Metal (331) 21 21 16 20 20 21 21 22 22 15

Machinery (333) 17 14 9 13 15 13 14 17 15 8

Electrical Equip. (335) 11 8 5 11 12 8 11 12 9 5

Trnsport. Equip. (336) 20 19 12 20 19 18 20 20 19 10

Petro/Coal (324) 11 9 8 11 11 9 11 11 9 8

Plastics (326) 10 8 5 10 10 8 10 10 8 5

Fabricated Metal (332) 21 21 13 21 22 21 20 22 20 11

Computer/Electron (334) 14 11 7 15 15 11 15 15 11 8

Misc. (339) 13 11 6 11 12 10 11 13 11 4

R&D Services (541712) 7 7 5 7 6 6 7 7 6 3

Unduplicated Total 76 73 53 77 76 73 78 80 74 51

Counterfactual 

Industry 

D/D 

R/C 

Dev 

R/C 

Mfctr 

R/C 

Deplo 

R/C 

D/D 

Prob 

Dev 

Prob 

Mfctr 

Prob 

D/D 

Dur 

Dev 

Dur 

Mfctr 

Dur 

Deplo 

Dur 

Chemical (325) 14 13 9 7 14 14 10 15 15 10 8 

Prim. Met. (331) 19 17 18 13 18 18 19 18 19 19 14 

Machinery (333) 10 12 10 6 10 11 10 10 12 11 6 

Elec. Equip. (335) 8 9 7 4 9 10 7 9 10 8 4 

Trnsp. Eqp. (336) 16 16 17 10 17 17 17 18 17 17 10 

Petro/Coal (324) 8 9 8 7 8 7 6 8 9 8 7 

Plastics (326) 8 9 8 5 8 9 8 8 9 8 5 

Fab. Metal (332) 17 17 18 11 16 16 16 16 18 18 11 

Comp. Elec. (334) 12 12 10 6 12 12 10 13 12 10 7 

Misc. (339) 10 10 9 4 10 10 9 10 10 9 3 

R&D (541712) 6 6 5 3 6 6 5 7 6 5 3 

Unduplicat. Total 65 64 62 44 66 65 62 68 70 66 46 

Note: Interviewees were allowed to indicate multiple industries. Numbers in this table reflect the number of 

interviewees in each industry answering each question. Totals in this table are unduplicated, counting each 

interviewee only once, no matter how many industries that interviewee is in; therefore, numbers in each column 

sum (over industries) to more than the total. R&D stages: D/D, discovery/design; Dev, development; Mfctr, 

manufacturing; Deplo, deployment. Model parameters: R/C, relative Cost; Prob, probability of advancing to next 

stage; Dur, duration. These parameters correspond to Table 2 (Actual Scenario) and Table 3 (Counterfactual 

Scenario) in the interview guide (provided in the appendix). Industries: 325, Chemical, excluding 

pharmaceuticals and medicines; 331, Primary metals; 333, Machinery; 335, Electrical Equipment, Appliances, 

and Components; 336, Transportation Equipment, including aerospace; 324, Petroleum and Coal Products; 326, 

Plastics and Rubber Products; 332, Fabricated Metal Products; 334, Computer and Electronic Products, including 

semiconductor and other electronic components; 339, Miscellaneous Manufacturing, including medical devices; 

541712, Physical, Engineering, and Life Sciences (except biotechnology) R&D. In the calibration of the actual 

scenario model, relative cost in the discovery/design stage is normalized at 1. 

As an example, for transportation equipment, the average of 

responses to question V.5 was 30.5%. Domestic R&D for this 

industry was $45.972 billion in 2013, 30.5% of which is 

$14.019 billion. 
Section 3 — Methods and Analysis Approach 

3-17

Step 2. Converting to Dollar-Denominated Costs: Analogy 

Principle. For each industry, relative costs for each R&D stage, 

under the actual and counterfactual scenarios, were converted 

to dollar-denominated costs by drawing an analogy between 

the total relative out-of-pocket cost in the calibrated model for 

an industry and the total annual R&D expenditure for that 

industry. The method is most easily illustrated by example. 

Continuing with the example of transportation equipment, 

(unitless) relative costs are shown together with dollardenominated costs in Table 3-5. Dollar-denominated costs were 

calculated based on an analogy: Total relative out-of-pocket 

costs of 159.6 in the calibrated model representing the current 

environment with existing infrastructure (actual scenario) were 

assumed to be analogous to actual R&D expenditures of 

$14,019 million per year. Then, every other cell in the table of 

dollar-denominated costs is obtained by multiplying the 

corresponding relative cost by the ratio of $14,019 to 159.6. 

Table 3-5. Converting to Dollar-Denominated R&D Costs, Transportation Equipment 

Type of Costs 

Discovery & 

Design Development Manufacturing Deployment Total 

Relative (unitless) 

Out-of-pocket, actual 46.6 35.7 40.3 36.9 159.6 

Capitalized, actual 110.8 65.4 57.9 41.8 275.9 

Out-of-pocket, 

counterfactual 

8.8 8.0 12.7 25.5 54.9 

Capitalized, 

counterfactual 

15.6 12.2 16.6 27.9 72.2 

Dollar-denominated (millions of 2013 U.S. dollars), based on $13.895 billion annual out-ofpocket R&D expenditure 

Out-of-pocket, actual 4,091 3,137 3,545 3,246 14,019 

Capitalized, actual 9,734 5,746 5,090 3,670 24,240 

Out-of-pocket, 

counterfactual 

774 699 1,113 2,237 4,823 

Capitalized, 

counterfactual 

1,367 1,070 1,455 2,454 6,346 

Note: Dollar-denominated costs in the lower part of the table were calculated based on an analogy: Total relative 

out-of-pocket costs of 134.2 in the calibrated model representing the current environment with existing 

infrastructure (actual scenario) were assumed to be analogous to actual R&D expenditures of $13,895 per year. 

Then, every other cell in the table of dollar-denominated costs is obtained by multiplying the corresponding 

relative cost by the ratio of $13,895 to 134.2. 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

3-18

For example, in the “Discovery & Design” column, multiplying a 

relative capitalized actual cost of 110.8 by $14,019 and dividing 

by 159.6 yields a capitalized actual cost of $9,734. Completing 

the table and comparing total capitalized costs, actual with 

counterfactual, improved infrastructure is estimated to save 

$17.9 billion in capitalized R&D costs per year for the 

transportation industry (the difference between $24.2 billion 

actual and $6.3 billion counterfactual). 

Adding Value to R&D Outcomes. The value of R&D efficiency 

impacts is only part of the total value of an improved Materials 

Innovation Infrastructure. This value, as we have estimated it, 

is the value companies could realize by targeting the same R&D 

outcomes they would have without the improved infrastructure. 

But when companies are faced with opportunities to perform 

R&D more efficiently (at lower cost for a given outcome), it 

stands to reason that they would respond by targeting more 

ambitious R&D outcomes, aiming to deploy superior materials 

at a higher rate. By choosing this response, companies would 

reveal that they can realize greater value that way. 

Through interviews with industry experts, we were able to test 

this hypothesis and quantify the additional value improved R&D 

infrastructure could enable companies to capture—by

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

leveraging that R&D to commercialize improved products and 

new product lines, and expand into new markets (Section IV of 

the interview guide, provided in the appendix). 

 3.5 CALCULATING CONFIDENCE INTERVALS 

As is clear from the description in Section 3.2 of the R&D cost 

model, key measurements of interest are nonlinear functions of 

interviewees’ quantitative responses. What is more, at least 

some of these quantitative responses are unlikely to be 

normally distributed. For example, transition probabilities are 

constrained to be between 0 and 1. These features complicate 

the calculation of confidence intervals. 

A bootstrap approach is therefore used to provide confidence 

intervals for all quantitative measures of interest. The bootstrap 

approach involves generating additional (pseudo) data using 

the information from the original data, then drawing inferences 

from the pseudo data about the distribution of a measure of 

interest. The pseudo data are generated by resampling with 
Section 3 — Methods and Analysis Approach 

3-19

replacement from the actual data to generate a large number 

(1,000 in this case) of pseudo samples. The measure of interest 

is then calculated from each of those samples, generating 

1,000 pseudo observations of the measure. A confidence 

interval for the measure of interest can then be calculated from 

those 1,000 pseudo observations. 

Recall from Section 3.3 that improved infrastructure is 

estimated to save $16.8 billion in capitalized R&D costs per 

year for the transportation industry. The bootstrap procedure 

can be explained by example, by deriving the 95% confidence 

interval for this estimate step by step. 

Step 1. Each of our 121 observations was assigned an integer 

weight (i.e., 0, 1, 2, ...), drawn from the binomial distribution, 

which is a convenient way of simulating sampling with 

replacement. The weight assigned to an observation represents 

the number of times the observation is “drawn” or selected into 

the pseudo sample. One set of 121 weights defines one pseudo 

sample. 

Step 2. We performed a full analysis on the pseudo sample, 

deriving averages of each parameter of the R&D process model, 

total out-of-pocket and capitalized costs for both the actual and 

counterfactual scenarios, the differences between the costs in 

the two scenarios, etc. Each measure of interest for that 

pseudo sample was stored. 

Step 3. Steps 1 and 2 were repeated 1,000 times, resulting in 

1,000 pseudo observations of each measure of interest. For 

example, after completing Step 3, we had generated 1,000 

pseudo observations of the capitalized R&D cost savings (i.e., 

the difference in capitalized R&D cost between the actual and 

counterfactual scenarios) for the transportation industry. These 

pseudo observations fell on either side of our actual observation 

of $17.9 billion. For the calculations that follow, it is helpful to 

use the more exact figure of $17.894 billion. 

Step 4. The 1,000 pseudo observations were sorted, and the 

25th and 976th largest for each measure of interest were stored. 

For capitalized cost savings for the transportation industry, x25

= $11.341 billion ($6.553 billion less than our estimate) and 

x976 = $25.509 billion ($7.615 billion more than our estimate). 

Because the true expected value of the 1,000 pseudo

observations is equal to our estimate of $17.894 billion, we can 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

3-20

say that there is a 2.5% chance that a given observation is at 

least $6.553 billion less than the true expected value and also a 

2.5% chance that a given observation is at least $7.615 billion 

more than the true expected value. 

That is equivalent to saying there is a 2.5% chance that the 

true expected value is at least $6.553 billion more than a given 

observation and also a 2.5% chance that the true expected 

value is at least $7.615 billion less than a given observation. 

Therefore, a 95% confidence interval for our point estimate of 

$17.894 billion would be between $10.279 billion ($7.615 less 

than $17.894) and $24.447 billion ($6.553 more than

$17.894). 
4-1

Industry Needs 

Assessment 

Through preliminary scoping interviews with industry experts and 

review of the relevant literature, issue briefs, and industry 

reports, as discussed in Section 3.1, we identified six broad areas 

of industry need to be addressed by an improved Materials 

Innovation Infrastructure. Table 4-1 presents these areas of need 

and provides illustrative examples of the types of infrastructure 

technology that could address each one and the types of 

potential impacts improved infrastructure could be expected to 

have. 

Experts interviewed—both for the preliminary scoping 

interviews and the more structured, guided interviews—

stressed the strong complementarity among the six areas of 

need and the consequent overlap among the types of 

infrastructure and potential impacts. One important policy 

implication of this complementarity is that infrastructure 

investments should be planned with all of the six areas in mind; 

to ignore any one of the six would reduce the value realized 

from investments aimed at addressing the others. 

Access to high-quality data emerged as the most important 

need and one of the most difficult for industry to address 

exclusively through private investment (Figures 4-1 and 4-2). 

Moreover, through qualitative discussions, access to highquality data emerged as a linchpin of a Materials Innovation 

Infrastructure. Access to high-quality data is a prerequisite for 

model validation and uncertainty quantification and for the 

productive application of machine learning, modeling and 

simulation, and many other elements of an envisioned Materials 

Innovation Infrastructure. In discussions of the other five 

identified needs and the kinds of infrastructure that could help 

address them, interviewees often returned to the primacy of 

Access to high-quality 

data emerged as a 

linchpin of a Materials 

Innovation Infrastructure. 

Access to high-quality 

data is a prerequisite for 

model validation and 

uncertainty quantification 

and for the productive 

application of machine 

learning, modeling and 

simulation, and many 

other elements of an 

envisioned Materials 

Innovation Infrastructure. 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

4-2

high-quality data to emphasize that “this other thing [i.e., the 

specific infrastructure technology under discussion] does not 

work without it.” 

Table 4-1. Technology Infrastructure Needs for Advanced Materials Innovation 

Industry Need 

Examples of Infrastructure 

Technology to Address Need Potential Impacts 

Access to HighQuality Data

Nonproprietary 

experimental data, 

computational data, 

and software code

 Fundamental materials data 

 Data standardization and curation 

 Models underpinning accurate and 

repeatable material measurement 

 More easily leverage prior research 

with less duplication of effort 

 Enable greater reliance on more 

efficient computational approaches 

Collaborative 

Networks 

Efficient means of 

sharing materials 

information (e.g., 

along a supply 

chain, among 

research 

collaborators) 

 Methods for capturing, 

characterizing, and sharing 

materials data in structured 

formats 

 Communication standards and 

translators (“MT Connect for 

material measurement 

equipment”) 

 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 

Material Design 

Methods

Shorter paths to 

better starting 

points 

Inverse modeling 

capability 

 Models, simulations, and 

metrologies for advanced 

materials design 

 Machine learning tools 

 Enable more targeted searches of 

design space for promising 

candidate materials 

 Identify more novel materials, 

breaking out of the search for 

incremental improvements to find 

more distant global optima 

Production & ScaleUp

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-to-roll 

printing, directed self-assembly) 

 Reduce trial and error when scaling 

up (from lab scale to pilot scale, 

from pilot scale to production scale) 

 Overcome the “Valley of Death” 

between lab scale and production 

scale: pilot-scale manufacturing 

services and facilities are 

underprovided by the market 

(continued) 
Section 4 — Industry Needs Assessment 

4-3

Table 4-1. Technology Infrastructure Needs for Advanced Materials Innovation (continued) 

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 

 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) 

 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) 

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 

 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 

 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 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

4-4

Figure 4-1. 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 4-2. 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. 

3% 3% 5% 9%

2%

9%

5% 7%

8% 5%

9%

12%

10% 12% 17% 18% 27%

16%

39% 34% 37% 41%

30%

43%

43% 45% 34% 27%

32%

20%

Access to HighQuality Data

(mean: 4.1)

Quality

Assurance &

Component

Certification

(mean: 4.1)

Model

Validation &

Uncertainty

Quantification

(mean: 3.9)

Materials

Design

Methods

(mean: 3.7)

Production &

Scale-Up

(mean: 3.8)

Collaborative

Networks

(mean: 3.5)

5 - critically

4 - very

3 - moderately

2 - somewhat

1 - not important

4% 4% 2% 5% 5%

11%

5% 15% 9% 5% 11%

19%

30% 29% 29% 26% 24% 34%

41% 35% 39% 43% 42%

25%

19%

18%

21% 21% 19%

12%

Access to HighQuality Data

(mean: 3.6)

Quality

Assurance &

Component

Certification

(mean: 3.5)

Model

Validation &

Uncertainty

Quantification

(mean: 3.7)

Materials

Design

Methods

(mean: 3.7)

Production &

Scale-Up

(mean: 3.6)

Collaborative

Networks

(mean: 3.1)

5 - prohibitively

4 - very

3 - moderately

2 - somewhat

1 - not difficult
Section 4 — Industry Needs Assessment 

4-5

All of the elements of the envisioned Materials Innovation 

Infrastructure have strong public-good content. Companies 

therefore have weak incentives to develop these technologies 

themselves. This essential insight was borne out in interviews 

with industry experts. Beyond the public-good content of 

infrastructure technologies, the principal cross-cutting 

justification for public investment, voiced most often by 

industry experts as the reason identified needs could not be 

effectively addressed through private investment, was the 

multidisciplinarity required to meet the needs. Collaboration is 

needed among computer scientists, statisticians, data analysts, 

and materials engineers with both computational and

experimental expertise. This multidisciplinary expertise is found 

only in some larger companies. Industry experts, even those at 

large, diversified companies, stressed that the depth of 

expertise and close collaboration across multiple disciplines 

required to develop a Materials Innovation Infrastructure is 

more than they have in-house and is a reason they value the 

involvement of the national laboratories. 

The difficulty of meeting a need through private investment, for 

which ratings are summarized by Figure 4-2, could reflect 

either technical difficulty (i.e., the difficulty of developing a 

solution with the R&D facilities and personnel at the company’s 

disposal) or the difficulty of justifying investment in developing 

a solution or buying a solution from outside the company. The 

difficulty rating is intended to capture industry experts’ 

perceptions of the importance of public investment: Is this a 

need that the private sector can adequately address on its own 

(reflected in a lower difficulty rating), or is there a compelling 

argument for public investment in infrastructure technology 

(reflected in a higher difficulty rating)? 

All six areas of need were perceived to be “very” or “critically” 

important by at least 60% of respondents, led by access to 

high-quality data (82%) and quality assurance, quality control, 

and component certification (78%). Five of the six needs were 

perceived to be “very” or “prohibitively” difficult to address 

through private investment by at least 50% of respondents, led 

by materials design methods (64%), followed by production 

and scale-up, model validation and uncertainty quantification, 

and access to high-quality data (all tied at 60%). Figures 4-1 

and 4-2 summarize these results. 

To justify the need for 

public investment in 

Materials Innovation 

Infrastructure, industry 

experts emphasized its 

public-good content and 

the multidisciplinarity 

required to develop it. 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

4-6

When respondents rated something as being relatively 

unimportant to them, one or more of three broad reasons was 

typically offered, no matter which area of need they are 

discussing: 

1. They deem the technology to be underdeveloped for 

their purposes, either because they are unaware of 

recent data and model improvements or because they 

work with materials for which these tools are less fully 

developed (this viewpoint was especially common for

ceramics, electronics, and biomaterials). 

To paraphrase one interviewee: “Computational 

methods have difficulty dealing with the statistical 

probability of defects, their location, and the statistical 

imprecision of manufacturing processes, whether 

making aluminum or composites.” 

2. They think that their status quo of operations is efficient 

enough. This attitude seemed to come most often from 

respondents with exclusively experimental backgrounds. 

3. They are worried about keeping information proprietary 

and protecting their intellectual property (although we 

tried to emphasize voluntary exchange of 

precompetitive, or nonproprietary, information and data 

in follow-up questions with these respondents, it was 

still a concern often heard). 

Common themes and key takeaways from the qualitative 

insights provided by respondents are discussed below for each 

identified area of need. 

 4.1 ACCESS TO HIGH-QUALITY DATA 

The identified need for access to high-quality data refers to 

precompetitive as opposed to proprietary data. Although 

companies must also produce data specific to proprietary 

materials they are developing, they can often benefit from 

nonproprietary, or nonmarket-oriented, data on related generic 

materials, which companies can use to develop computational 

models. For instance, a company could use high-quality data on 

a generic material (like the now standard 4130 steel alloy) to 

develop and calibrate a computational model, verify that its 

predictions are accurate, and quantify the uncertainty or 

variability associated with its predictions. The company could 

then use the model to simulate the properties of a proprietary 

material it is developing (like a new alloy that strives to offer 

improvements in terms of performance characteristics or cost). 
Section 4 — Industry Needs Assessment 

4-7

In general, improved access to high-quality data will enable 

companies to rely increasingly on computational approaches to 

materials design in situations where doing so can reduce costs, 

accelerate timelines, and improve outcomes. 

Fundamental materials data can be generated by experiments 

(experimental data, which are generated from observations on 

actual physical materials) or by computer simulations 

(computational data, which are generated by computational 

models). High-quality data include not only measured 

quantities but also information about the precision of the 

measurements and the methods by which they were obtained. 

Computational data are most useful when accompanied by the 

software code used to generate the data. 

Besides fundamental materials data, organized in standard 

reference datasets, examples of infrastructure that could 

improve access to high-quality materials data include methods 

for data standardization and curation and the models 

underpinning accurate and repeatable material measurement. 

For example, several respondents noted the need for improved 

sensors that are able to automatically collect and upload data 

generated through experiments or in production lines. Such 

tools would automate the data generation and organization 

process, improving the quality and efficiency of data collection 

and reducing the cost of contributing to private or common 

high-quality materials databases. 

Industry experts indicated that a shortage of high-quality data 

“drives conservatism” and slows down the innovation process. 

Without high-quality data that could be used to evaluate new 

candidate materials early on in the discovery/design phase 

when the cost of failure is lower, companies are unwilling to 

take chances for fear that technical failures in later R&D stages 

will be too costly. Without the ability to effectively screen 

candidate materials early on, the expected cost of attempting 

to develop a new material is prohibitive, and companies are 

more likely to stay with materials they know. Materials 

innovation then tends to be limited to incremental 

improvements without attempts at breakthrough innovation. 

One materials engineer described it as “local optimization” as 

opposed to “global optimization”—locating slightly higher 

ground near where you are already standing (on the surface of 

an objective function) instead of striking out to find a higher 

A shortage of highquality data “drives 

conservatism” and slows 

down the innovation 

process. 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

4-8

hilltop farther away. In his analogy, high-quality data are (an 

essential part of) the telescope that helps you find the higher 

hilltop in the distance. Improved access to high-quality data 

would lead to a greater number and diversity of ideas being 

explored in the discovery/design stage. 

A common remark was that “the data are out there” but not 

really accessible. Either the data are not in digital form (e.g., 

underlying published results and represented in tables and 

figures in online articles, and therefore prohibitively costly to 

extract on a useful scale, given currently available tools) or else 

they are behind a pay wall. The upshot is that only very large 

companies can afford to access high-quality digital data.9 Small 

companies are shut out of (“can’t play” in) certain markets 

because their customers want them to be using the same data 

and models as the larger companies. This point was 

emphasized not only by small companies but also by 

downstream customers who felt locked into buying from a few 

large companies. For these downstream customers, the 

difficulty faced by smaller prospective suppliers in accessing 

high-quality data represented a “weakness in their supply 

chain.” 

With improved access to high-quality data, companies could 

better leverage published research with less need to replicate 

the underlying experiments, enabling them to more quickly 

focus on the most promising compositions and processes as the 

starting point for developing a new material. In the current 

environment, the same experiments are being repeated again 

and again by different researchers, each extracting only the 

information they need. Variations in experimental conditions—

without the means to combine data across all of the different 

experiments—reduce the quality, or information content, of the 

body of published results produced by these experiments. In 

these conditions, replication wastes resources. With 

standardization of experimental conditions—not necessarily 

making all experiments the same but rather enabling

experimenters to measure and record all relevant differences in 

a standardized way—and the means of measuring, recording, 

and digitally publishing all of the information that can be 

captured from a given experiment, this situation would change: 

 

9

 Notable exceptions exist where small companies focusing on labscale discovery and design rely heavily on commercial data and 

modeling tools. 

With improved access to 

high-quality data, 

companies could better 

leverage published 

research without needing 

to replicate experiments, 

enabling them to more 

quickly focus on the most 

promising compositions 

and processes as the 

starting point for 

developing a new 

material. 
Section 4 — Industry Needs Assessment 

4-9

Some degree of replication would be productive, because the 

data from each experiment could be combined with that from 

related experiments to yield better information (e.g., improving 

the accuracy and precision of a given measurement or 

prediction); truly redundant experiments (doing the same 

experiment again only to capture information that was not 

collected from previous ones) would become unnecessary, 

freeing research resources for more productive uses. 

Industry experts emphasized that being able to trust and use 

published research is critically important to companies, for 

which the replication of published research can be a 

significant—and for some potential R&D projects a prohibitive—

cost at the discovery/design stage. From structural alloys to 

nanomaterials, industry researchers reported roughly a 50% 

“hit rate” when trying to reproduce published results. The other 

half of the time, misses were generally attributable to an 

omitted step or an incorrect interpretation in the published 

research. In nanomaterials, where results can depend on atomlevel purity, researchers have encountered published results of 

“successful” experiments that were later found to have been 

the result of contaminants. 

Interviewees’ ratings of the importance and difficulty of 

meeting this need did not vary significantly across materials 

classes (Figures 4-3 and 4-4). 

Figure 4-3. Interviewees’ Rating of Importance of Access to High-Quality Data 

Note: The distribution of ratings is shown for the six materials classes represented by at least 20 interviewees. 

42% 43% 50% 48% 50% 47%

42% 41%

42% 45% 46% 42%

Lightweight

and Structural

Ceramics Polymers Polymer

Composites

Electronic and

Photonic

Energy Storage

5 - critically

4 - very

3 - moderately

2 - somewhat

1 - not important
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

4-10

Figure 4-4. Interviewees’ Rating of Difficulty of Addressing the Need for High-Quality Data 

through Private Investment 

Note: The distribution of ratings is shown for the six materials classes represented by at least 20 interviewees. 

 4.2 COLLABORATION NETWORKS 

The collaborative networks envisioned as part of an improved 

Materials Innovation Infrastructure would provide an efficient 

means of voluntarily sharing materials information across tiers 

of supply chains or among research collaborators. As 

envisioned, these networks would help address industry’s need 

for high-quality experimental data, computational data, and 

software code by providing a means of connecting disparate but 

related datasets and making it possible to locate relevant data 

with targeted searches.10

Ultimately, collaborative networks could alter the incentives 

facing academic researchers, with positive impacts on the 

translation of public science into commercial applications. 

Proponents envision a means of tracking the use of digital data 

and software code that attaches credit to its originator. In this 

way, researchers who produce relevant, well-interpreted, 

reproducible data and results, which are then used by others, 

will receive recognition analogous to that which attaches to 

publication and citation today. The idea is that by providing a 

 

10 Collaboration networks envisioned as part of the MGI are distinct 

from the similar-sounding “innovation clusters” much discussed in 

innovation policy circles. Innovation clusters are physically 

collocated firms and universities, whereas collaboration networks 

are virtual, geographically dispersed, having nodes connected by 

the internet. 

37% 42% 45% 46% 43% 50%

20% 14% 11% 11% 9%

15%

Lightweight

and Structural

Ceramics Polymers Polymer

Composites

Electronic and

Photonic

Energy Storage

5 - prohibitively

4 - very

3 - moderately

2 - somewhat

1 - not difficult
Section 4 — Industry Needs Assessment 

4-11

means of recognizing more relevant contributions to materials 

science and engineering, collaborative networks can alter 

incentives and thereby change behavior, increasing the 

productivity of research, measured in terms of its translation 

into commercial applications and ultimate impacts. 

Keeping proprietary information private and protecting their 

intellectual property was highlighted by many respondents as 

the key deterrent to taking part in collaborative networks, even 

among those who considered improved collaborative networks 

to be important. For example, a company producing a range of 

metal alloy materials stated that collaborative networks have a 

direct benefit for companies like theirs, but only if they can 

protect their intellectual property while contributing. 

Respondents generally perceived a useful role for a neutral 

body with technical expertise in facilitating interactions between 

potentially competing entities. 

Collaborative networks were less likely to receive the highest 

ratings for importance or difficulty. Notably, however, 

respondents who did rate collaborative networks highly in both 

respects were more likely to emphasize the importance of 

network externalities in realizing the benefits of MGI 

approaches: the more other organizations in their supply chains 

invested in MGI approaches, the greater the return on their 

own investments. In the context of these discussions, 

respondents counted universities among these organizations in 

two important respects, both of which could be enhanced by 

collaborative networks. First, academic research was a source 

of fundamental knowledge, which collaborative networks could 

enhance by making digital data more available and its quality 

(especially its reproducibility) easier to assess. Second, these 

respondents emphasized the importance of universities as a 

source of talented scientists and engineers: new hires out of 

graduate programs and consultants and collaborators among 

faculty. 

Interviewees’ ratings of the importance and difficulty of 

meeting this need vary only slightly across materials classes 

(Figures 4-5 and 4-6). Differences were not statistically 

significant, and qualitative discussions in interviews revealed no 

notable justification for them, so these differences among 

materials classes should not be overinterpreted. 

Respondents who rated 

collaborative networks 

highly were more likely to 

emphasize the importance 

of network externalities in 

realizing the benefits of 

MGI approaches: the 

more other organizations 

in their supply chains 

invested in MGI 

approaches, the greater 

the return on their own 

investments. 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

4-12

Figure 4-5. Interviewees’ Rating of Importance of Collaborative Networks 

Note: The distribution of ratings is shown for the six materials classes represented by at least 20 interviewees. 

Figure 4-6. Interviewees’ Rating of Difficulty of Provisioning Collaborative Networks 

through Private Investment 

Note: The distribution of ratings is shown for the six materials classes represented by at least 20 interviewees. 

 4.3 MATERIALS DESIGN METHODS 

High-quality materials data are food for computational 

modeling and simulations. These computational materials 

design methods are needed to analyze the data and generate 

testable hypotheses and predictions that aid the process of 

designing and developing new materials. 

Commercial computational software packages are available but 

expensive, making them accessible only to (typically larger) 

36% 49% 39% 45%

29%

53%

28%

18% 29% 24%

21%

21%

Lightweight

and Structural

Ceramics Polymers Polymer

Composites

Electronic and

Photonic

Energy Storage

5 - critically

4 - very

3 - moderately

2 - somewhat

1 - not important

25% 24% 24% 21% 21%

42%

17% 12% 11% 10% 4%

5%

Lightweight and

Structural

Ceramics Polymers Polymer

Composites

Electronic and

Photonic

Energy Storage

5 - prohibitively

4 - very

3 - moderately

2 - somewhat

1 - not difficult
Section 4 — Industry Needs Assessment 

4-13

companies with large R&D budgets. Many respondents indicated 

that even if their companies were able to afford the tools 

needed for improved materials design, they lack the internal 

expertise to use such tools. A typical comment was that “the 

tools exist, but they require a highly specialized modeler to use 

them, and our company’s business model, or the business 

model of a typical company in our supply chain, does not 

support a dedicated full-time employee who is so highly 

specialized.” 

Moreover, the need for materials design methods is broader 

than modeling software. As industry’s needs for high-quality 

data and collaborative networks are met, the potential 

increases for machine learning (i.e., artificial intelligence) to 

identify related datasets, uncover correlations, and generate 

new hypotheses by analyzing massive amounts of data. 

Realizing this vision requires collaboration among computer 

scientists, statisticians, data analysts, and materials scientists 

and engineers. This breadth of expertise is rarely found all in 

one place in U.S. industry, but it is found in U.S. national 

laboratories. One industry expert was especially blunt: “No U.S. 

company can develop multiscale modeling frameworks,11 nor 

would they have the incentive to attempt to do so. But many 

companies would benefit from having this capability.” 

Interviewees’ ratings of the importance and difficulty of 

meeting this need vary only slightly across materials classes 

(Figures 4-7 and 4-8). Differences were not statistically 

significant, and qualitative discussions in interviews revealed no 

notable justification for them. Therefore, these differences 

among materials classes should not be overinterpreted. 

 

11 An example of infrastructure technology offered in the interview 

guide, multiscale modeling frameworks are analytic means of 

linking macroscopic process models with microscopic materials 

simulations. 

Realizing a vision of 

using machine learning 

approaches for materials 

discovery requires 

collaboration among 

computer scientists, 

statisticians, data 

analysts, and materials 

scientists and engineers. 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

4-14

Figure 4-7. Interviewees’ Rating of Importance of Materials Design Methods 

Note: The distribution of ratings is shown for the six materials classes represented by at least 20 interviewees. 

Figure 4-8. Interviewees’ Rating of Difficulty of Provisioning Infrastructure for Materials 

Design Methods through Private Investment 

Note: The distribution of ratings is shown for the six materials classes represented by at least 20 interviewees. 

 4.4 PRODUCTION AND SCALE-UP 

Computational approaches that could reduce failure rates in 

later R&D stages, especially the scale-up that happens in the 

manufacturing stage, could have enormous impact. Failures at 

this stage can be extremely costly because of the amounts of 

materials used and the costs of setting up large-scale 

production runs. There is also the large sunk cost of earlier R&D 

stages at stake. 

50%

33% 43% 45% 40% 44%

27%

29%

27% 24% 35% 28%

Lightweight

and Structural

Ceramics Polymers Polymer

Composites

Electronic and

Photonic

Energy Storage

5 - critically

4 - very

3 - moderately

2 - somewhat

1 - not important

40% 44% 43% 39% 48%

28%

15%

21% 22% 18%

24%

28%

Lightweight and

Structural

Ceramics Polymers Polymer

Composites

Electronic and

Photonic

Energy Storage

5 - prohibitively

4 - very

3 - moderately

2 - somewhat

1 - not difficult
Section 4 — Industry Needs Assessment 

4-15

Key to preventing costly failures at this stage is to be able to 

predict them and then go forward with expensive physical 

testing only when computational models predict a high 

likelihood of success. For some materials classes, like metal 

alloys, good process models are available and commonly used. 

In others, like polymer composites or biomaterials, such models 

are less well developed. Typical of materials classes and 

applications where models are available and widely used is that 

the properties of the bulk materials are more readily translated 

into good predictions of the materials’ performance

characteristics in a product application. Models of the way a 

material behaves at a molecular level (e.g., in nanotechnology 

applications) or of material behavior at interfaces between thin 

layers of different materials are comparatively less mature and 

therefore less widely used in industry. 

Interviewees across seemingly very different industries and 

applications emphasized the difficulty of scaling the production 

of a new material. The common denominator here seemed to 

be the company’s size. Small, innovative startups, whether 

developing advanced structural alloys or nanotechnology 

components for flexible touchscreens or batteries, typically 

need to partner with other companies after the discovery/ 

design stage. For the metal alloy developer, the challenge is 

finding a foundry that will do pilot-scale runs. For the developer 

of a touchscreen or flexible battery material, the challenge is 

putting their product into a prototype device with all of the 

other components it needs to function, if only in a rudimentary 

way for demonstration purposes. 

As examples of other countries’ proactive efforts to address this 

issue, interviewees referenced the Industrial Technology 

Research Institute (ITRI) of Taiwan, the Electronics and 

Telecommunications Research Institute (ETRI) of South Korea, 

and the Fraunhofer institutes in Germany. Fraunhofer’s 67 

institutes had a research budget in 2015 of more than 2.1 

billion euros ($2.2 to $2.4 billion U.S. dollars in 2015), 30% of 

which comes from German state and federal governments; by 

comparison, U.S. government investment in the Manufacturing 

Innovation Institutes of Manufacturing USA is roughly one-tenth 

Interviewees across 

seemingly very different 

industries and 

applications emphasized 

the difficulty of scaling 

the production of a new 

material. 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

4-16

that amount.12 Although the respective policy scopes of 

Fraunhofer and Manufacturing USA are not perfectly analogous, 

the magnitude of the funding difference loomed large for many 

industry respondents. 

Interviewees’ ratings of the importance and difficulty of 

meeting this need did not vary significantly across materials 

classes (Figures 4-9 and 4-10). 

Figure 4-9. Interviewees’ Rating of Importance of Provisioning Infrastructure for 

Production and Scale-Up 

Note: The distribution of ratings is shown for the six materials classes represented by at least 20 interviewees. 

 

12 According to the 2015 annual report of Fraunhofer-Gesellschaft, 

almost 30% of its 2.1-billion-euro annual research budget “is 

contributed by the German federal and state governments in the 

form of base funding, enabling the institutes to work ahead on 

solutions to problems that will not become relevant to industry and 

society until five or ten years from now.” Manufacturing USA 

institutes are funded by the U.S. government through cooperative 

agreements, with the federal funding level between $70 million and 

$110 million (https://www.manufacturing.gov/funding/). 

37%

21% 11% 15% 19%

6%

31%

38% 46% 44% 38%

39%

Lightweight

and Structural

Ceramics Polymers Polymer

Composites

Electronic and

Photonic

Energy Storage

5 - critically

4 - very

3 - moderately

2 - somewhat

1 - not important
Section 4 — Industry Needs Assessment 

4-17

Figure 4-10. Interviewees’ Rating of Difficulty of Provisioning Infrastructure for Production 

and Scale-Up through Private Investment 

Note: The distribution of ratings is shown for the six materials classes represented by at least 20 interviewees. 

 4.5 QUALITY ASSURANCE AND CONTROL AND 

COMPONENT CERTIFICATION 

Before a new material can be made available as a commercial 

product and used in commercial applications, its manufacturer 

must develop quality-control practices. For applications critical 

to health and safety, such as implantable medical devices and 

aerospace, new products must undergo certification or 

qualification testing. New materials may themselves be 

subjected to certification or qualification testing before being 

used in the actual product. 

Computational modeling and simulation can help reduce the 

cost of quality assurance, quality control, and qualification and 

certification testing. Application of computational approaches 

for this purpose begins in the discovery/design R&D stage, 

where researchers working with small amounts of material can 

gain early insight into its susceptibility to defects at 

manufacturing scale. 

For example, if it is difficult to completely eliminate variation in 

a material’s composition or microstructure at manufacturing 

scale, early research may look for materials with performance 

characteristics (such as strength and conductivity) that are 

comparatively robust to small variations in those attributes that 

are difficult or expensive to control at manufacturing scale. 

43% 45% 43% 44% 40% 39%

20% 19% 20% 22% 25% 33%

Lightweight and

Structural

Ceramics Polymers Polymer

Composites

Electronic and

Photonic

Energy Storage

5 - prohibitively

4 - very

3 - moderately

2 - somewhat

1 - not difficult
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

4-18

Modeling and simulation could not only find the “hilltops” of an 

objective function in design-attribute space but also describe 

how steeply that hilltop falls away in different dimensions. If 

certain dimensions are difficult or expensive to control at 

manufacturing scale, the best material might be one that 

attains a slightly lower hilltop that falls away gradually instead 

of the highest peak with a precipitous drop off. 

In later stages of R&D, computational models can be used to 

forecast manufacturing variation and, by understanding its 

determinants, better control it. 

Physical testing of materials is expensive. Material samples, 

batches, or parts must be manufactured, often on a large scale, 

to undergo testing. Computational modeling and simulation can 

reduce the overall cost of physical testing by reducing failure 

rates. A common misconception is that the objective is to 

reduce the need for physical testing—to actually reduce the 

number of successful tests that must be performed before the 

material is accepted—by relying instead on high-fidelity 

simulations. Although reducing the need for physical testing 

may be a part of the story in some applications, it is generally 

not where the greatest potential impact is to be found. Rather, 

the proposition is to be more certain of a successful outcome 

before beginning the expensive process of physical testing, 

saving time and material that would otherwise be wasted on 

failed tests. 

These points apply equally to product acceptance testing at the 

point of market transactions as well as to R&D operations. As 

part of the manufacturing stage of R&D, a company will 

perform tests to ensure a newly developed manufacturing 

process is producing the new material to the correct 

specifications. Buyers must likewise be satisfied that the 

material’s performance characteristics meet their specifications. 

Being able to accurately and efficiently measure compliance 

with performance specifications therefore has important 

implications even after a material’s first use in a commercial 

application, which defined the end of the deployment stage in 

our R&D process model; it affects a new material’s rate of 

diffusion into multiple commercial applications. 

A common theme, especially notable for additive 

manufacturing, semiconductors, and nanomaterials, was the 

need to assess the quality, in terms of purity or other 

Computational modeling 

and simulation can 

reduce the overall cost of 

physical testing by 

reducing failure rates, 

enabling companies to 

better predict the 

outcomes of physical 

testing and go forward 

only when there is high 

confidence of success. 
Section 4 — Industry Needs Assessment 

4-19

attributes, of incoming raw materials. Nanomaterials 

manufacturing, for instance, requires extremely high purity for 

reagents, and it is typical to have to rely on suppliers who are 

focused on larger customers with less stringent requirements, 

making it necessary for nanomaterials developers to do their 

own quality control testing on raw materials after acquiring 

them from upstream suppliers. 

Interviewees’ ratings of the importance and difficulty of 

meeting this need did not vary significantly across materials 

classes (Figures 4-11 and 4-12). Although its importance rating 

is lower for energy storage materials, no justification for such a 

difference emerged from the interviews, so it should not be 

overinterpreted. 

Figure 4-11. Interviewees’ Rating of Importance of Quality Assurance/Control and 

Component Certification 

Note: The distribution of ratings is shown for the six materials classes represented by at least 20 interviewees. 

38% 38%

19% 21% 21% 16%

40% 38%

54% 57% 63%

42%

Lightweight

and Structural

Ceramics Polymers Polymer

Composites

Electronic and

Photonic

Energy Storage

5 - critically

4 - very

3 - moderately

2 - somewhat

1 - not important
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

4-20

Figure 4-12. Interviewees’ Rating of Difficulty of Provisioning Infrastructure for Quality 

Assurance/Control and Component Certification through Private Investment 

Note: The distribution of ratings is shown for the six materials classes represented by at least 20 interviewees. 

 4.6 MODEL VALIDATION AND VERIFICATION 

AND UNCERTAINTY QUANTIFICATION 

Computational models and simulations are only useful if they 

make good predictions. No model makes predictions with 

perfect accuracy and precision. Being able to understand and 

quantify a model’s accuracy and precision is therefore critical 

for making good use of it.13

Developing a general architecture and specific tools for 

uncertainty quantification to address this need requires a 

combination of statistical analytic and materials engineering 

expertise that is not 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 

for model validation and uncertainty quantification. Industry 

experts who characterized this issue saw the potential to 

address it by leveraging “significant untapped capabilities” in 

national laboratories. 

 

13 Although uncertainty quantification, or UQ, is the term that has 

taken hold, its meaning is actually closer to precision quantification: 

describing in quantitative terms how far an estimate or prediction is 

likely to be from the “true” value of something. 

36% 42% 37% 46%

30% 28%

17% 10% 17%

12%

17% 22%

Lightweight and

Structural

Ceramics Polymers Polymer

Composites

Electronic and

Photonic

Energy Storage

5 - prohibitively

4 - very

3 - moderately

2 - somewhat

1 - not difficult
Section 4 — Industry Needs Assessment 

4-21

Industry experts expressed a keen interest in better tools to 

meet this need. Materials scientists and engineers who perceive 

the benefits of computational modeling and simulation in their 

work said that better tools in this area would be a “force 

multiplier,” greatly enhancing the value they can leverage from 

computational approaches to materials design. 

On a pragmatic note, many emphasized the need to make a 

business case for investment in computation; they see 

uncertainty quantification and model validation and verification 

as key to securing the buy-in of business leadership in their 

companies. As one interviewee put it (to paraphrase), “these 

capabilities are critical because, when you are asking for 

funding, management has to be able to evaluate the expected 

return on investment. You have to be able to demonstrate 

tangible value and also identify the risks and uncertainties.” 

From the interviews, it was clear that this need has significant 

overlap with the need for high-quality materials data. A typical 

comment was that lack of validation data presented a 

bottleneck. A repository of measured basic properties (e.g., 

band gaps, conductivities, structural properties) of different 

materials classes would be enormously valuable. Materials used 

for validation are nonproprietary. Even the largest companies 

(large, diversified multinational manufacturers with R&D 

laboratory capacity) have weak incentives to direct their 

experimental groups to generate this kind of basic data, but it 

is an essential step to be able to trust computational models. 

Clean, verified characterization data for different materials 

classes from an unimpeachable source are absolutely critical. 

To paraphrase one interviewee: “If you want to predict ionic 

transport in a polymer, there’s a shortage of papers that 

reliably and reproducibly provide all the data we need to 

validate our models. We therefore have to ask for experiments 

to be done again and again. A central repository of many 

properties for many materials classes would fill this need.” 

Interviewees’ ratings of the importance and difficulty of 

meeting this need vary only slightly across materials classes 

(Figures 4-13 and 4-14). Differences were not statistically 

significant, and qualitative discussions in interviews revealed no 

notable justification for them, so these differences among 

materials classes should not be viewed as significant. 

Lack of validation data 

presents a bottleneck. A 

repository of measured 

basic properties (e.g., 

band gaps, conductivities, 

structural properties) of 

different materials classes 

would be enormously 

valuable. 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

4-22

Figure 4-13. Interviewees’ Rating of Importance of Model Validation/Verification and 

Uncertainty Quantification 

Note: The distribution of ratings is shown for the six materials classes represented by at least 20 interviewees. 

Figure 4-14. Interviewees’ Rating of Difficulty of Provisioning Infrastructure for Model 

Validation/Verification and Uncertainty Quantification through Private Investment 

Note: The distribution of ratings is shown for the six materials classes represented by at least 20 interviewees. 

41%

30% 28% 37%

25% 26%

28% 43% 44%

41%

50% 47%

Lightweight

and Structural

Ceramics Polymers Polymer

Composites

Electronic and

Photonic

Energy Storage

5 - critically

4 - very

3 - moderately

2 - somewhat

1 - not important

40% 44% 40% 41% 32% 33%

13%

19%

11% 15%

5% 11%

Lightweight and

Structural

Ceramics Polymers Polymer

Composites

Electronic and

Photonic

Energy Storage

5 - prohibitively

4 - very

3 - moderately

2 - somewhat

1 - not difficult
5-1

Economic Impact 

Assessment 

The potential economic impact estimates reported here are 

based on a comparison of the materials R&D process in the 

current environment (actual scenario) with the process in a 

hypothetical environment with an improved Materials

Innovation Infrastructure (counterfactual scenario). The actual 

and counterfactual scenarios were characterized by assigning 

values to the parameters of the R&D process model 

(Figure 5-1) based on the opinions and perceptions of industry 

experts. 

Section 5.1 presents the model calibrations in the actual and 

counterfactual scenarios, looking at overall averages (across 

industries and materials classes) of R&D model parameters. 

Section 5.2 presents overall economic impact estimates. 

Section 5.3 presents results for individual industries and 

materials classes. 

Figure 5-1. Materials R&D Process Stages 

Note: Reprised from Figure 3-1 for ease of reference, the materials R&D process stages are based on the 

Quantitative Benchmark for Time to Market (QBTM) analytical framework, developed for NIST by Nexight Group 

and Energetics (2016). 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

5-2

 5.1 CALIBRATED R&D PROCESS MODEL 

Tables 5-1 and 5-2 show estimated parameters of the R&D 

process model in the actual scenario (Table 5-1) and in the 

counterfactual scenario with improved infrastructure 

(Table 5-2). Confidence intervals were estimated using the 

bootstrap approach explained in Section 3.5. 

Potential Impacts on Risk. Companies developing new 

materials face the risk that an 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 improved 

infrastructure: for every 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 5-2). 

Estimated potential impacts on transition probabilities are 

greater earlier in the R&D process. 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; that is a 48% 

chance of deployment, conditional on reaching the development 

stage, with improved infrastructure versus a 35% chance in the 

current environment. 

Table 5-1. Materials R&D Process Model Parameter Estimates: Actual Scenario 

Model 

Parameter 

R&D Stage 

Discovery & 

Design Development Manufacturing Deployment 

Relative cost 

per year 

1 3.9 

(3.3, 4.5) 

16.5 

(9.1, 23.4) 

11.9 (5.5, 19.8) 

Probability of 

advancing to 

next stage 

29%

(25%, 34%) 

48% 

(44%, 54%) 

72% 

(67%, 78%) 

N/A 

Duration 

(Years) 

2.6

(2.1, 3.2) 

3.0

(2.5, 3.6) 

2.0

(1.6, 2.4) 

2.5

(1.8, 3.3) 

Note: The relative cost per year in the discovery and design stage is normalized at 1. For simplicity, projects that 

begin a stage are assumed to complete that stage. These numbers reflect the average responses from all

interviews. For a response related to a given R&D stage to be included in these averages, the respondent must 

have reported expertise in the R&D stage. In parentheses are 95% confidence intervals. 
Section 5 — Economic Impact Assessment 

5-3

Table 5-2. Materials R&D Process Model Parameter Estimates: Counterfactual Scenario 

Model 

Parameter 

R&D Stage 

Discovery & 

Design Development Manufacturing Deployment 

Relative cost 

per year 

0.7 

(0.6, 0.8) 

2.2 

(1.8, 2.6) 

8.5 

(3.9, 13.1) 

8.6 

(3.7, 13.7) 

Probability of 

advancing to 

next stage 

42%

(36%, 47%) 

60% 

(55%, 65%) 

80% 

(74%, 85%) 

N/A 

Duration 

(years) 

1.4

(1.1, 1.7) 

2.0

(1.5, 2.6) 

1.4

(1.2, 1.7) 

1.8

(1.3, 2.4) 

Note: For simplicity, projects that begin a stage are assumed to complete that stage. These numbers reflect the 

average responses from all interviews. For a response related to a given R&D stage to be included in these 

averages, the respondent must have reported expertise in the R&D stage. In parentheses are 95% confidence 

intervals. 

Figure 5-2. Potential Impact on Successful Transition Rates 

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 9.7 to 4.8 (a 21% 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 2.7 to 2.0 (a 50% chance of deployment, 

conditional on reaching the development stage, with improved infrastructure versus a 36% chance currently). 

9.8

2.9

1.4

1.0

5.0

2.1

1.3

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

Discovery/Design Development Manufacturing Deployment

R&D Projects in Stage per Deployment

Existing

Infrastructure

Improved

Infrastructure
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

5-4

Potential Impacts on Timelines. We estimate that 

development of a new material takes on average 10.2 years 

and that an acceleration of 3.5 years could be possible with 

improved infrastructure (Figure 5-3). That is an estimated 

potential 35% acceleration (95% C.I. 29% to 41%). We 

estimate that the duration of the discovery and design stage 

could be cut from 2.6 years to 1.4 years, an average potential 

acceleration of 48% (95% C.I. 39% to 59%) Our results 

suggest that the first stage of materials R&D may be done in 

half the time with improved infrastructure but that the three 

subsequent stages cannot be accelerated as much, at least not 

on average in the cross section of industries represented by our 

group of experts. 

Figure 5-3. Potential Impact on Time to Market 

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. 

Potential Impacts on Relative Costs per Project per Year. 

To enable aggregation across respondents and avoid potentially 

sensitive questions of actual R&D expenditure focused on 

materials innovation, the cost per project per year in the 

discovery/design stage in the current environment was 

normalized at 1.0. Then, respondents were asked to quantify 

the cost per project per year in the other stages, both in the 

current environment and the counterfactual environment with 

improved infrastructure, in relative terms. 

2.6

1.4

3.0

2.0

2.0

1.4

2.5

1.8

0 2 4 6 8 10

Existing

Infrastructure

Improved

Infrastructure

Years from Start of Discovery/Design Stage

Discovery/Design Development Manufacturing Deployment

3.5-year 

acceleration
Section 5 — Economic Impact Assessment 

5-5

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 5-4). 

Figure 5-4. 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, respectively, roughly four times and 

three times more cost-intensive than the development stage, which is, in turn, roughly four times more costintensive 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. 

 5.2 POTENTIAL ECONOMIC IMPACT ESTIMATES 

The R&D process model was calibrated separately for each 

industry for which R&D expenditures are reported in the NSF 

R&D survey (National Science Foundation, 2016). Industryspecific potential impacts were estimated from these 

calibrations, following the steps described in Section 3.3, and 

then summed to obtain total economic impact estimates. 

Overall, the potential impacts in transition rates, time to 

market, and relative costs attributed to an improved Materials 

Innovation Infrastructure achieve a 65% reduction in out-ofpocket R&D cost (95% confidence interval [C.I.] 56% to 74%) 

and a 71% reduction in the capitalized cost per successful 

deployment (95% C.I. 62% to 79%). 

1.00 0.75

3.90 2.16

16.52

8.55

11.95

8.59

0

5

10

15

20

25

30

35

Existing

Infrastructure

Improved

Infrastructure

Relative Cost per Project per Year

Deployment

Manufacturing

Development

Discovery/Design
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

5-6

On average, in the current environment, $1 million in out-ofpocket R&D cost represents $1.46 million in capitalized R&D 

cost (95% C.I. 1.37 to 1.54), assuming an 8% cost of capital. 

With improved infrastructure, under the same assumptions, $1 

million in out-of-pocket cost represents $1.24 million in 

capitalized cost (95% C.I. 1.19 to 1.29). 

The relative burden of financial capital cost is lower with 

improved infrastructure for two main reasons. First, improved 

infrastructure shortens development time, reducing the time 

over which capital costs accrue. Second, improved 

infrastructure reduces risk; a disproportionate share of this 

potential impact is realized in the earlier stages,

discovery/design and development, which carry relatively more 

capital costs because of their longer time to market 

(Figure 5-5). 

To estimate the economic value of these relative R&D cost 

savings to the U.S. economy, we multiplied percentage savings 

by that part of U.S. R&D expenditure focused on materials 

innovation.14 Out of a total U.S. R&D expenditure of $168.3 

Figure 5-5. Potential Impact on Out-of-Pocket and Capitalized R&D Cost per Deployment 

Note: Out-of-pocket R&D cost per new material deployment in the current environment is normalized at 1.0. 

Capitalized costs include the opportunity cost of financial capital. 

 

14 These steps are explained in Section 3.4. 

0.19 0.04

0.38

0.06

0.25

0.07

0.39

0.09

0.34

0.12

0.45

0.14

0.22

0.12

0.24

0.12

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Existing

Infrastructure

Improved

Infrastructure

Existing

Infrastructure

Improved

Infrastructure

Relative Cost per Deployment

Deployment

Manufacturing

Development

Discovery/Design

Out-of-Pocket Capitalized
Section 5 — Economic Impact Assessment 

5-7

billion per year15 across 10 manufacturing industries and one 

relevant service industry (Physical, Engineering, and Life 

Sciences R&D, excluding biotechnology), between $41.2 billion 

and $60.8 billion was estimated to be the subset to which the 

relative impacts attributable to improved infrastructure could be 

applied. Based on the perceptions of the industry experts 

interviewed, this is the total R&D effort, summed across their 

respective industries, focused on advanced materials 

innovation. These out-of-pocket R&D expenditures translate to 

capitalized R&D costs of between $60.0 billion and $93.6 billion. 

Table 5-3. R&D Expenditure and Savings by Industry (Millions of 2013 U.S. Dollars/Year) 

Industry 

R&D 

Expenditure 

Materials R&D 

Expenditure 

Out-of-Pocket R&D 

Cost Savings 

Capitalized R&D 

Cost Savings 

Computer/Electron (334) 67,205 19,186 

(11,462, 27,321) 

12,611 

(6,211, 18,275) 

21,390 

(8,791, 30,275) 

Transport. Equip. (336) 45,972 14,019 

(10,743, 17,588) 

9,196 

(5,050, 12,924) 

17,894 

(10,279, 24,447) 

Chemical (325) 9,238 4,973 

(3,650, 6,228) 

3,690 

(2,620, 5,375) 

4,584 

(3,107, 6,262) 

R&D Services (541712) 8,910 3,734 

(2,052, 5,398) 

3,072 

(1,892, 4,783) 

5,326 

(2,937, 8,090) 

Machinery (333) 12,650 3,644 

(1,831, 5,677) 

1,444 

(-420, 2,860) 

2,101 

(-685, 4,372) 

Misc. (339) 13,509 2,845 

(1,565, 4,076) 

1,713 

(645, 2,662) 

2,501 

(1,118, 3,826) 

Electrical Equip. (335) 4,136 1,135 

(610, 1,660) 

639 

(170, 1,071) 

955 

(211, 1,574) 

Plastics (326) 3,650 693 

(316, 999) 

491 

(110, 893) 

627 

(124, 1,081) 

Fabricated Metal (332) 2,212 635 

(415, 839) 

433 

(226, 625) 

742 

(322, 1,101) 

Primary Metal (331) 624 231 

(160, 301) 

163 

(99, 241) 

253 

(163, 366) 

Petroleum and Coal (324) 242 76 

(41, 110) 

34 

(0, 69) 

50 

(-1, 96) 

Total 168,348 51,172 

(41,219, 60,762) 

33,486 

(25,253, 42,150) 

56,421 

(38,846, 68,836) 

Note: In parentheses are 95% confidence intervals. Industries: 325, Chemical, excluding pharmaceuticals and 

medicines; 331, Primary metals; 333, Machinery; 335, Electrical Equipment, Appliances, and Components; 336, 

Transportation Equipment, including aerospace; 324, Petroleum and Coal Products; 326, Plastics and Rubber 

Products; 332, Fabricated Metal Products; 334, Computer and Electronic Products, including semiconductor and 

other electronic components; 339, Miscellaneous Manufacturing, including medical devices; 541712, Physical, 

Engineering, and Life Sciences (except biotechnology) R&D. R&D expenditure is 2013 domestic R&D, from NSF 

(2016, Table 23). 

 

15 Based on 2013 BRDIS (National Science Foundation, 2016). 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

5-8

For each industry, Table 5-3 shows total R&D expenditure, 

estimated R&D expenditure related to developing and applying 

new materials, estimated out-of-pocket R&D cost savings, and 

estimated capitalized R&D cost savings. Overall, R&D 

efficiencies attributable to improved infrastructure are 

estimated to generate cost savings of between $39 billion and 

$69 billion per year, or between 62% and 79%. 

Percentage savings, in terms of out-of-pocket costs (the ratio of 

estimated out-of-pocket R&D cost savings to estimated R&D 

expenditure related to developing and applying new materials), 

range from 40% to 80% (Figure 5-6). Overall savings are 

dominated by the two largest industries in terms of R&D: 

Computer and Electronic Products (334), which includes 

semiconductor and other electronic components, and 

Transportation Equipment (336), which includes aerospace. 

Figure 5-6. Materials R&D Expenditure and Percentage Savings 

Note: Estimated materials R&D expenditures are indicated by gray bars situated on the horizontal axis (confidence 

intervals are provided in Table 5-3). Estimated percentage savings are indicated by the black tick marks, with 

95% confidence intervals indicated by floating blue bars. 

0%

20%

40%

60%

80%

100%

0

4,000

8,000

12,000

16,000

20,000

Percentage Savings

Materials R&D Expenditure (Millions of 

2013 U.S. Dollars)
Section 5 — Economic Impact Assessment 

5-9

Adding Value to R&D Outcomes. Potential R&D efficiency 

impact estimates are calculated by holding R&D outcomes 

constant; these are estimates of how much less it could cost, 

with improved Materials Innovation Infrastructure, to generate 

the same number and quality of new materials deployed per 

year on average. 

This is not the whole story because it does not account for the 

increase in the rate of innovation we would expect to see as 

companies respond to the new environment in which R&D is 

more efficient. As the cost of developing and deploying new 

materials falls, U.S. companies would be expected to develop 

and deploy new materials at a higher rate. The resulting 

benefits, for the innovating companies and for downstream 

companies and end users of the products made from those new 

materials, would stem from the R&D efficiency impact and so 

multiply the total potential impact associated with improved 

infrastructure. 

To address this issue, structured interviews explored the ways 

in which an improved Materials Innovation Infrastructure would 

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 (see Section IV of the interview guide, provided in the 

appendix). 

The following few examples are broadly representative of 

industry experts’ justification for multiplying the potential R&D 

efficiency impacts:16

 Better models would enable targeted materials design, 

which would allow manufacturers to design new 

products faster and incorporate new materials into the 

product design process earlier on. Taking this idea even 

further, improved modeling capability could enable the 

processes of designing of new materials and new 

product applications to be integrated, so the new 

material is designed to meet the emerging requirements 

of the new product as the new product is designed to 

take fullest advantage of the properties of the new

material. 

 Models help in understanding the underlying physics and 

designing better, more focused experiments. Not only 

 

16 These comments are paraphrased. 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

5-10

can these benefits of modeling reduce the cost of 

developing and deploying a new material, but they can 

also improve the performance attributes of the new 

material. For example, consider an R&D project to 

develop lighter alloys for engine components. With 

improved infrastructure, we would expect to be able to 

achieve the same R&D outcome at lower cost, but our

approach in this new environment would more likely be 

to invest the same amount in R&D and get a better 

outcome—a lighter and more durable material for lighter 

and more durable engines. 

 Better ways of finding new materials with unique 

properties would enable companies to expand their 

design portfolio, which could significantly enhance the 

quality of the products they supply or open up new 

markets. Transformative research is much more 

important than the ability to improve the efficiency of 

research efforts. 

 For small startups especially, built around a new 

advanced material that may take upwards of 10 years to 

develop, securing investment capital is often difficult. 

Shorter time to market and lower risk (i.e. lower 

attrition rates) could make new materials more 

attractive investments for U.S. venture capitalists, 

increasing the number of materials R&D projects that 

can be sustained through deployment. 

Overall, interviewees estimated the value of such potential 

impacts to be 2 to 3 times as large as the value of the R&D 

efficiency impacts. Taken together with the R&D efficiency 

impacts, the potential economic benefit of an improved 

Materials Innovation Infrastructure is estimated to be between 

$123 billion and $270 billion per year. 

Table 5-4 provides estimates of potential R&D cost savings and 

the added value of improved R&D outcomes broken out by 

industry. 
Section 5 — Economic Impact Assessment 

5-11

Table 5-4. Potential Economic Impacts by Industry (Millions of 2013 U.S. Dollars Per Year) 

Industry 

Capitalized R&D 

Cost Savings 

Added Value of 

Improved R&D 

Outcomes 

Total Potential 

Economic Impact 

Computer/Electron (334) 21,390 

(8,791, 30,275) 

62,771 

(18,251, 102,105) 

84,160

(29,899, 129,310) 

Transport. Equip. (336) 17,894 

(10,279, 24,447) 

36,913 

(15,323, 56,037) 

54,807

(28,058, 78,315) 

Chemical (325) 4,584 

(3,107, 6,262) 

11,506 

(5,397, 19,564) 

16,091

(8,828, 25,206) 

R&D Services (541712) 5,326 

(2,937, 8,090) 

17,263 

(8,087, 33,668) 

22,589

(12,787, 39,295) 

Machinery (333) 2,101 

(-685, 4,372) 

7,983 

(-5,523, 19,267) 

10,083

(-5,557, 23,677) 

Misc. (339) 2,501 

(1,118, 3,826) 

8,698 

(1,677, 17,922) 

11,199

(3,265, 21,142) 

Electrical Equip. (335) 955 

(211, 1,574) 

2,070 

(-77, 3,677) 

3,025

(274, 5,171) 

Plastics (326) 627 

(124, 1,081) 

2,020 

(-1,572, 4,430) 

2,646

(-1,433, 5,440) 

Fabricated Metal (332) 742 

(322, 1,101) 

1,876 

(797, 3,240) 

2,618

(1,146, 4,262) 

Primary Metal (331) 253 

(163, 366) 

251 

(142, 385) 

504

(322, 716) 

Petroleum and Coal (324) 50 

(-1, 96) 

97 

(-41, 221) 

147

(-44, 311) 

Total 56,421 

(38,846, 68,836) 

151,447 

(82,515, 203,036) 

207,869

(123,229, 270,047) 

Note: In parentheses are 95% confidence intervals. Industries: 325, Chemical, excluding pharmaceuticals and 

medicines; 331, Primary metals; 333, Machinery; 335, Electrical Equipment, Appliances, and Components; 336, 

Transportation Equipment, including aerospace; 324, Petroleum and Coal Products; 326, Plastics and Rubber 

Products; 332, Fabricated Metal Products; 334, Computer and Electronic Products, including semiconductor and 

other electronic components; 339, Miscellaneous Manufacturing, including medical devices; 541712, Physical, 

Engineering, and Life Sciences (except biotechnology) R&D. 

 5.3 INDUSTRY AND MATERIALS CLASS RESULTS 

The R&D process model was calibrated separately for industries 

and for materials classes.17 This section gives an overview of 

some of those industry-specific and material-specific results. 

R&D is inherently risky in that the outcomes of R&D trials are 

uncertain. The risk associated with R&D outcomes in the 

present environment was found not to vary significantly across 

industries (Figure 5-7). Responses of interviewees developing 

chemicals and new materials for machinery suggest that these 

industries’ average attrition rates, reflected in the number of 

projects that must enter the discovery/design stage for every 

 

17 The steps of this process are explained in Section 3.3. 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

5-12

new material successfully deployed, are less than other 

industries’ rates. The number of materials development 

projects entering the pipeline for every new material deployed 

is 4.5 for chemicals (95% confidence interval 2.4 to 6.2) and 

6.3 for machinery (95% confidence interval 2.6 to 9.4) 

compared with the overall average of 9.7 (95% confidence 

interval 7.3 to 12.2). For the one nonmanufacturing industry 

considered—physics, engineering, and life sciences R&D—the 

number of projects per deployment is 32.0, but with a 95% 

confidence interval of 16.2 to 54.7 it is not significantly 

different from the overall average. For all other industries, 95% 

confidence intervals also include 9.7. 

Figure 5-7. Number of Discovery/Design Projects per New Material Deployed by Industry 

Note: Points are indicated by the black tick marks, with 95% confidence intervals indicated by floating bars. 

The average risk reduction—or attrition reduction, reflected in a 

lower number of projects needing to enter the R&D pipeline for 

every new material deployed—is 51% fewer projects overall 

-10

0

10

20

30

40

50

Number of Discovery/Design Projects per New Material 

Deployed
Section 5 — Economic Impact Assessment 

5-13

(95% confidence interval 43% to 62%). Transportation 

equipment is the only industry to differ significantly from the 

overall average, with a 69% reduction (95% confidence interval 

59% to 81%); all other industries’ confidence intervals contain 

the overall average of 51%. For two industries, electrical 

equipment and components and petroleum and coal products, 

the reduction is not significantly different from zero 

(Figure 5-8). 

Figure 5-8. Percentage Reduction in Number of Discovery/Design Projects by Industry 

Note: Points are indicated by the black tick marks, with 95% confidence intervals indicated by floating bars. 

Across all industries, the average total duration of the four R&D 

stages is 10.0 years (95% confidence interval 8.2 years to 11.7 

years). Three industries have significantly shorter average 

durations, shown in Figure 5-9: chemicals, at 5.0 years (95% 

confidence interval 3.1 years to 6.7 years), miscellaneous, 

-100%

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

Percentage Reduction in Number of Discovery/Design Projects per 

New Material Deployed
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

5-14

including medical devices, at 5.5 years (95% confidence 

interval 4.6 years to 6.6 years), and plastics, at 6.2 years (95% 

confidence interval 4.6 years to 7.7 years). 

Figure 5-9. Total Duration of R&D Stages by Industry 

Note: Points are indicated by the black tick marks, with 95% confidence intervals indicated by floating bars. 

The relative acceleration averages 33% across industries (95% 

confidence interval 27% to 40%). Although the relative 

acceleration varies across industries, as shown in Figure 5-10, 

the difference is only significant for physics, engineering, and 

life sciences R&D, with a 48% reduction (95% confidence 

interval 40% to 59%). The estimated improvement is not 

significant for the machinery industry. 

The data underlying Figures 5-7, 5-8, 5-9, and 5-10 are shown 

in Table 5-5. 

0

5

10

15

20

25

Years from Start of Discovery/Design to End of Deployment 

Stage
Section 5 — Economic Impact Assessment 

5-15

Figure 5-10. Percentage Reduction in Total Duration of R&D Stages by Industry 

Note: Points are indicated by the black tick marks, with 95% confidence intervals indicated by floating bars. 

Table 5-5. R&D Risk and Duration, Baseline and Potential Impact, by Industry 

Industry 

Baseline 

Number of 

Discovery/ 

Design 

Projects per 

New Material 

Deployed 

Percentage 

Reduction in 

Number of 

Discovery/Design 

Projects per New 

Material Deployed

Baseline Total 

Duration of 

R&D Stages 

(Years) 

Percentage 

Reduction in 

Total Duration 

of R&D Stages 

Overall Average 9.7 

(7.3, 12.2) 

51%

(43%, 62%) 

10.0 

(8.2, 11.7) 

33%

(27%, 40%) 

Electrical Equip. (335) 11.8 

(−0.7, 22.7) 

37%

(−17%, 96%) 

9.0 

(6.0, 13.4) 

24%

(7%, 39%) 

Computer/Electron (334) 9.6 

(4.0, 13.6) 

58%

(47%, 69%) 

12.5 

(7.8, 16.6) 

37%

(26%, 48%) 

Primary Metal (331) 12.8 

(6.1, 17.6) 

52%

(36%, 68%) 

12.5 

(8.4, 16.6) 

36%

(24%, 46%) 

(continued) 

-40%

-20%

0%

20%

40%

60%

Percentage Reduction in Years from Start of Discovery/Design 

to End of Deployment Stage
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

5-16

Table 5-5. R&D Risk and Duration, Baseline and Potential Impact, by Industry (continued) 

Industry 

Baseline 

Number of 

Discovery/ 

Design 

Projects per 

New Material 

Deployed 

Percentage 

Reduction in 

Number of 

Discovery/Design 

Projects per New 

Material Deployed

Baseline Total 

Duration of 

R&D Stages 

(Years) 

Percentage 

Reduction in 

Total Duration 

of R&D Stages 

Plastics (326) 8.3 

(5.1, 10.7) 

37%

(4%, 60%) 

6.2 

(4.6, 7.7) 

35%

(13%, 53%) 

Misc. (339) 9.5 

(6.2, 13.3) 

50%

(12%, 109%) 

5.5 

(4.6, 6.6) 

35%

(21%, 53%) 

Fabricated Metal (332) 13.8 

(5.4, 22.7) 

57%

(35%, 85%) 

11.6 

(7.3, 15.3) 

43%

(28%, 60%) 

Machinery (333) 6.3 

(2.6, 9.4) 

27%

(2%, 63%) 

10.1 

(2.2, 17.0) 

2%

(−38%, 43%) 

Transport. Equip. (336) 14.0 

(6.1, 22.2) 

69%

(59%, 81%) 

11.1 

(7.7, 14.5) 

34%

(25%, 44%) 

Petroleum and Coal (324) 6.8 

(−0.4, 12.2) 

−11%

(−118%, 86%) 

7.5 

(3.5, 10.9) 

29%

(19%, 38%) 

Chemical (325) 4.5 

(2.4, 6.2) 

43%

(29%, 57%) 

5.0 

(3.1, 6.7) 

29%

(14%, 44%) 

R&D Services (541712) 32.0 

(6.2, 54.7) 

61%

(34%, 100%) 

13.4 

(4.2, 21.3) 

48%

(40%, 59%) 

Note: In parentheses are 95% confidence intervals. Industries: 325, Chemical, excluding pharmaceuticals and 

medicines; 331, Primary metals; 333, Machinery; 335, Electrical Equipment, Appliances, and Components; 336, 

Transportation Equipment, including aerospace; 324, Petroleum and Coal Products; 326, Plastics and Rubber 

Products; 332, Fabricated Metal Products; 334, Computer and Electronic Products, including semiconductor and 

other electronic components; 339, Miscellaneous Manufacturing, including medical devices; 541712, Physical, 

Engineering, and Life Sciences (except biotechnology) R&D. 

Turning to materials classes, we note that catalysts and 

biomaterials are estimated to have lower attrition rates across 

R&D stages, while other materials classes are not significantly 

different from the overall average (Figure 5-11). The 

improvement (i.e., percentage reduction) in attrition rates that 

could be achieved with improved infrastructure is estimated to 

be significantly higher for two materials classes: polymer 

composites and caloric materials (Figure 5-12). The total 

duration of R&D stages is estimated to be significantly different 

only for biomaterials (Figure 5-13), and the potential 

improvement (i.e., acceleration) is significantly higher for 

caloric materials and organic electronics and photovoltaic 

materials and significantly lower for biomaterials (Figure 5-14). 

The data underlying Figures 5-11, 5-12, 5-13, and 5-14 are 

shown in Table 5-6. 
Section 5 — Economic Impact Assessment 

5-17

Figure 5-11. Number of Discovery/Design Projects per New Material Deployed by Materials 

Class 

Note: Points are indicated by the black tick marks, with 95% confidence intervals indicated by floating bars. 

Figure 5-12. Percentage Reduction in Number of Discovery/Design Projects by Materials 

Class 

Note: Points are indicated by the black tick marks, with 95% confidence intervals indicated by floating bars. 

-10.0

-5.0

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

Number of Discovery/Design Projects per New 

Material Deployed

0%

25%

50%

75%

100%

Percentage Change in Number of 

Discovery/Design Projects per New 

Material Deployed
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

5-18

Figure 5-13. Total Duration of R&D Stages by Materials Class 

Note: Points are indicated by the black tick marks, with 95% confidence intervals indicated by floating bars. 

Figure 5-14. Percentage Reduction in Total Duration of R&D Stages by Materials Class 

Note: Points are indicated by the black tick marks, with 95% confidence intervals indicated by floating bars. 

0.0

5.0

10.0

15.0

20.0

25.0

Years from Start of Discovery/Design to End 

of Deployment Stage

-20%

0%

20%

40%

60%

80%

Percentage Change in Years from Start of 

Discovery/Design to End of Deployment Stage
Section 5 — Economic Impact Assessment 

5-19

Table 5-6. R&D Risk and Duration, Baseline and Potential Impact, by Materials Class 

Industry 

Baseline 

Number of 

Discovery/ 

Design 

Projects per 

New Material 

Deployed 

Percentage 

Reduction in 

Number of 

Discovery/Design 

Projects per New 

Material Deployed 

Baseline 

Total 

Duration of 

R&D Stages 

(Years) 

Percentage 

Reduction in 

Total 

Duration of 

R&D Stages 

Biomaterials 6.1 

(2.5, 9.5) 

35%

(16%, 54%) 

5.9 

(3.5, 8.9) 

10%

(-13%, 30%) 

Catalysts 7.2 

(4.7, 9.9) 

51%

(38%, 64%) 

11.9 

(6.1, 18.7) 

38%

(30%, 47%) 

Energy storage 12.6 

(0.9, 22.6) 

50%

(8%, 90%) 

11.1 

(4.8, 16.9) 

33%

(16%, 52%) 

Photovoltaic 9.6 

(5.3, 12.8) 

60%

(47%, 72%) 

14.9 

(6.1, 25.2) 

45%

(40%, 50%) 

Polymers 9.8 

(5.1, 14.8) 

56%

(43%, 68%) 

10.0 

(6.9, 14.2) 

33%

(15%, 47%) 

Polymer composites 12.0 

(7.3, 16.0) 

65%

(52%, 78%) 

12.0 

(6.8, 17.8) 

32%

(14%, 49%) 

Organic electronic 16.0 

(6.1, 23.7) 

75%

(48%, 98%) 

8.7 

(5.4, 12.3) 

43%

(38%, 47%) 

Ceramics 8.1 

(5.0, 10.8) 

41%

(27%, 54%) 

8.1 

(6.3, 10.3) 

33%

(24%, 40%) 

Electronic and Photonic 

materials 

13.2 

(5.5, 18.3) 

56%

(29%, 86%) 

8.4 

(6.2, 10.9) 

46%

(31%, 60%) 

Lightweight and Structural 14.1 

(6.2, 19.9) 

58%

(41%, 73%) 

12.7 

(9.5, 15.5) 

30%

(21%, 37%) 

Caloric Materials 17.0 

(-9.7, 37.2) 

86%

(72%, 104%) 

9.6 

(6.2, 14.0) 

58%

(38%, 80%) 

Overall Average 9.7 

(7.2, 12.1) 

51%

(42%, 61%) 

10.0 

(8.4, 12.0) 

33%

(26%, 40%) 

Note: In parentheses are 95% confidence intervals. 

6-1

Conclusions 

This report presents estimates of the potential economic benefit 

of an improved Materials Innovation Infrastructure of between 

$123 billion and $270 billion per year, based on structured 

interviews with more than 100 industry experts (Table 6-1). 

Roughly a quarter these estimated benefits come from making 

the R&D process more efficient, saving time and reducing risk. 

The rest of the potential benefits are projected to come from 

companies responding to the new environment by targeting 

more ambitious R&D outcomes: undertaking R&D projects they 

would not otherwise have done, leveraging that R&D to 

commercialize improved products and new product lines, and 

expanding into new markets. The wide range of these estimates 

is to be expected for this type of analysis, asking a diverse 

group of experts to share their perceptions and opinions about 

the potential impact of improvements in infrastructure they can 

envision to meet their existing and emerging needs.

Table 6-1. Potential Economic Impact Estimates (Millions of 2013 U.S. Dollars Per Year) 

Type of Potential 

Impact Point Estimate 95% Confidence Interval 

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

Improved R&D 

Outcomes 151,447 (82,515, 203,036) 

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

Note: Potential R&D efficiency impact estimates are based on interview-based estimated impacts to the R&D 

process, summarized in Figures ES-3, ES-4, and ES-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 Section 3.5. 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. 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

6-2

The full economic benefits of improved infrastructure could be 

worth considerably more than the estimates provided here. 

Because these estimates were based on interviews with 

representatives of U.S. materials developers and 

manufacturers, the estimates reflect only that part of the 

benefits these companies expect to capture; these estimates 

exclude the additional benefits to consumers over and above 

the prices they pay for new products. For example, a new 

lightweight engine built of advanced alloys may cost more than 

a heavier conventional engine, but that price difference is likely 

to be much less than the fuel cost savings over the life of the 

lighter engine. This extra benefit to consumers of the 

lightweight engine is an example of consumer surplus not 

captured in the potential impact estimates presented here. 

Interviewees emphasized the need for high-quality materials 

data. The need for collaborative networks was most 

appreciated by those who viewed these networks as a means of 

improving access to high-quality data. Access to high-quality 

data was seen as the linchpin (necessary, albeit not sufficient 

by itself) to meeting the other four identified needs: 

 material design methods, enabling application of a 

systems approach to materials development, from 

discovery and design all the way through to deployment; 

 production and scale-up methods, including modelbased and simulation-based alternatives to expensive 

physical testing based on trial and error and faster, 

more cost-effective means of producing advanced 

materials at pilot scale and full scale; 

 quality assurance and control and component 

certification methods, enabling improved capabilities 

to model, predict, and control the formation of defects 

and to forecast manufacturing variation; and 

 model validation and uncertainty quantification, 

providing a basis for trust and acceptance of 

computational models and objective decision-making 

regarding reliance on computational analysis and 

simulation at a business level. 

A typical comment was that a lack of validation data presented 

a bottleneck, which could be improved by a publicly available 

repository of measured basic properties (e.g., band gaps, 

conductivities, structural properties) of different materials 

classes. Materials used for validation are nonproprietary. Even 
Section 6 — Conclusions 

6-3

at the largest companies (large, diversified multinational 

manufacturers with R&D laboratory capacity and the capability 

to generate this kind of data), companies have weak incentives 

to direct their experimental groups to generate basic, 

nonproprietary data, but it is an essential step to be able to 

trust computational models. Clean, verified characterization 

data for different materials classes from an unimpeachable 

source are absolutely critical. 

All six areas of need were rated very or critically important by 

at least 60% of respondents, led by access to high-quality data 

and quality assurance, quality control, and component 

certification. Five of the six needs were rated very or 

prohibitively difficult to address through private investment by 

at least 50% of respondents, led by materials design methods, 

production and scale-up, model validation and uncertainty 

quantification, and access to high-quality data. 

In Section 5, we provided what we believe to be the best 

available quantitative information on the materials innovation 

process: relative costs, attrition rates, and durations of four 

successive R&D stages. Out of more than 100 interviewees, 80 

provided quantitative estimates of these parameters, based on 

their experience and perceptions. These estimates provide a 

useful benchmark for analyzing a range of policy options. 

The policy focus of the present study was a range of 

infrastructure technology investments aimed at addressing the 

six identified needs. The exact nature of the technologies 

springing from those investments and the extent and speed of 

their impact on the identified needs were necessarily 

speculative. Nevertheless, out of the 80 interviewees offering 

parameter estimates for the R&D process model in the current 

environment, 70 also provided parameter estimates in a 

hypothetical environment with an enhanced Materials

Innovation Infrastructure. 

Industry experts anticipated that computational approaches to 

materials discovery and design will become increasingly 

universal. Although today companies can gain an advantage by 

being among the early adopters of these approaches, they 

foresee a future in which these approaches will become the 

standard in many manufacturing industries, and companies will 

not be able to survive without them. 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

6-4

Although these industry experts envision a future in which 

using computational approaches is part of most if not all 

manufacturers’ business models, they stressed that building the 

technological infrastructure—developing the tools and 

standards companies must have to be able to use these 

approaches—is not part of their business models. Therefore, 

public investment in Materials Innovation Infrastructure will 

become increasingly important for a globally competitive U.S. 

manufacturing sector. 

This report has documented the perceptions and opinions of 

experts in materials innovation, mostly representing U.S. 

industry, regarding gaps in the Materials Innovation 

Infrastructure that encumber innovation aimed at realizing 

many important next-generation applications and industries. 

The process of discovering, developing, and deploying 

advanced materials is complex and heterogeneous. So too is 

the array of public-good infrastructure technologies that are 

needed for a vibrant, innovative, globally competitive U.S. 

manufacturing sector to flourish. 

This report has taken a broad brush to these complex issues. 

Its intent is to draw out high-level insights that for the most 

part cut across specific industries and materials classes. 

Insights into specific stages of the materials innovation process, 

specific elements of an improved Materials Innovation 

Infrastructure, and needs specific to certain industries or 

company characteristics have been explored qualitatively to the 

extent possible within the scope of the study. 

Accordingly, this analysis can in no way substitute for the close 

interaction between NIST scientists and industry 

representatives by which the sorts of high-level needs 

characterized here can be translated into specific projects, 

programs, and initiatives within the national laboratories. 
R-1

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Appendix: 

Interview Guide 

A-1 

NIST Materials Genome Initiative Strategic Planning Study: Economic Analysis of 

National Needs for Technology Infrastructure to Support the Materials Genome 

Initiative 

Interview Guide 

The National Institute of Standards and Technology (NIST) in the U.S. Department of Commerce has 

contracted with RTI International to conduct an economic analysis of standards, measurement, and 

general purpose technology needs that inhibit efficient development and deployment of advanced 

materials by U.S. manufacturers. 

The objectives of this critical strategic planning study include: 

• Identify industry needs related to Materials Innovation Infrastructure. 

• Identify barriers/challenges to meeting these needs. 

• Estimate the economic impact of meeting these needs. 

• Review and prioritize public policy and investment options. 

Your perspectives will help guide NIST’s strategic planning and program development process. 

Participation in this analysis is confidential; only aggregated information will be included in any 

deliverables or communications. Your name and your company’s name will not be disclosed. We do not 

wish to discuss specific products, strategies, or technologies, but rather your thoughts about industry 

needs and how investments in technology infrastructure to meet those needs would affect your company 

and companies like yours. 

Our research products will be an economic analysis, final report, and presentation materials. All 

deliverables will be publicly available in 2018 and these will be shared with you as soon as they are 

released. 

If you have questions, please contact: 

• Alan O’Connor, Principal Investigator, RTI, 919-541-8841 or oconnor@rti.org 

• Courtney Silverthorn, NIST Project Officer, NIST, 301-975-4189 or courtney.silverthorn@nist.gov. 

This collection of information contains Paperwork Reduction Act (PRA) requirements approved by the Office of 

Management and Budget (OMB). Notwithstanding any other provisions of the law, no person is required to

respond to, nor shall any person be subject to a penalty for failure to comply with, a collection of information 

subject to the requirements of the PRA unless that collection of information displays a currently valid OMB 

control number. Public reporting burden for this collection is estimated to be 35 minutes per response, 

including the time for reviewing instructions, searching existing data sources, gathering and maintaining the 

data needed and completing and reviewing the collection of information. Send comments regarding this burden 

estimate or any aspect of this collection of information, including suggestions for reducing this burden, to the 

National Institute of Standards and Technology, Attn., Courtney Silverthorn, courtney.silverthorn@nist.gov, 

(301) 975-4189. The OMB Control Number is 0693-0033, with an expiration date of 06/30/2019. 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

A-2

I. RESPONDENT BACKGROUND 

We will be referring to a stylized depiction of the materials innovation process:18

1. Does your perspective cover all four stages, or is it focused within a subset of these stages? 

[ ] All stages 

[ ] Only the following stages (check all that apply) 

[ ] Discovery/Design [ ] Development [ ] Manufacturing [ ] Deployment 

Briefly describe the lifecycle of a typical project with which you are involved: 

2. What types of materials does your work involve? (check all that apply) 

[ ] Biomaterials [ ] Polymers [ ] Correlated Materials 

[ ] Catalysts [ ] Polymer Composites [ ] Electronic and Photonic Materials 

[ ] Energy storage [ ] Organic Electronic [ ] Lightweight and Structural 

[ ] Photovoltaic [ ] Ceramics [ ] Caloric Materials 

[ ] Other ________________________________________ 

Briefly describe the types of materials and broad applications (we do not need specific 

information about any proprietary material or product): 

3. Is your background primarily experimental or computational? 

[ ] Experimental [ ] Computational [ ] Spans both experimental and computational 

Briefly describe the roles of experimental and computational approaches in your work: 

 

18 Based on Quantitative Benchmark for Time to Market (QBTM) for New Materials Innovation: An Analytical 

Framework. 

mgi.nist.gov/sites/default/files/uploads/user124/QBTM%20Final%20Analytical%20Framework_011216.pdf. 
Appendix — Interview Guide 

A-3

II. INDUSTRY NEEDS ASSESSMENT 

Table 1 summarizes industry needs identified through scoping interviews. 

1. Please rate the importance to your company of addressing each area of unmet need. 

(Please briefly explain the relative importance of meeting these needs.) 

2. Please rate the difficulty for your company of meeting each need on its own, in terms of both the 

technical difficulty and the business difficulty (i.e., difficulty justifying investment within the 

business model). 

(Please briefly explain the relative difficulty of meeting these needs.) 

3. Are there other needs, of equal or greater importance, not included in Table 1? 

(If so, please describe the need, rate its importance and difficulty, and briefly explain.) 

Table 1. Industry Needs: Rating Importance and Difficulty 

Areas of Industry Need

Examples of Infrastructure Technology 

to Address Need

Importance:

5 = critically 

4 = very 

3 = moderately 

2 = somewhat 

1 = not important

Difficulty:

5 = prohibitively 

4 = very 

3 = moderately 

2 = somewhat 

1 = not difficult

i. Access to High-Quality Data:

– Precompetitive experimental data, 

computational data, and software code

– Data standardization and curation 

– Models underpinning accurate and repeatable 

material measurement 

 

ii. Collaborative Networks: 

– Efficient means of sharing materials 

information (e.g., along a supply chain, 

among research collaborators)

– Methods for capturing, characterizing, and 

sharing materials data in structured formats 

– Communication standards & translators (“MT 

Connect for material measurement 

equipment”) 

 

iii. Material Design Methods: 

– Inverse modeling capability 

– Shorter paths to better starting points

– Models, simulations, metrologies for 

advanced materials design 

– Machine learning tools 

 

iv. Production & Scale Up: 

– Model-based alternatives to expensive 

physical testing/trial & error for scale-up 

– Faster, cost-effective means of producing 

advanced materials at pilot and full scale 

– Multiscale modeling frameworks (integrating 

macroscopic process models with 

microscopic materials simulation) 

– Process technology platforms (e.g., cold 

sintering, additive, roll-to-roll, directed selfassembly) 

 

v. Quality Assurance/Control & 

Component Certification: 

– Ability to model, predict, and control 

formation of defects 

– Ability to forecast manufacturing variation

– 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) 

 

vi. Model Validation/Verification & 

Uncertainty Quantification: 

– Basis for trust & acceptance of 

computational models 

– Basis for objective decision making 

regarding reliance on computational 

analysis/simulation at a business level 

– 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 

 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

A-4

III. IMPACTS ON MATERIALS DEVELOPMENT PROCESS 

1. Given the current environment, please provide generic estimates (i.e., for a typical project) of the 

relative costs, transition probabilities, and durations of the four stages of materials innovation 

(Table 2). 

a. How costly is a year of each stage relative to the cost of a year of Discovery/Design? 

Assume the cost of a year in Discovery/Design is 1.

(For example: A relative cost of 2 at the Development stage would indicate that a year of 

Development is twice as expensive as a year of Discovery/Design.) 

b. What is the probability that a project that starts a given stage advances to the next stage? 

(For example, 0.75 at Development stage implies that a project that starts the 

Development stage has a 75% chance of advancing to enter the Manufacturing stage.) 

c. About how many years does it take to complete each stage? 

2. How would your answers change in an improved environment where the needs discussed in 

Section II are addressed? (Table 3). 

Note that Relative Cost is not necessarily 1 at the Discovery/Design stage in Table 3. 

Entering a value of 1 would indicate that the cost per year in the Discovery/Design stage 

would be unaffected by improvements in infrastructure. 

3. Finally, please describe how changes would come about. How are potential impacts tied to 

improved infrastructure? 

Table 2. Materials Innovation Process in the Current Environment (Existing Technology 

Infrastructure) 

Discovery/Design Development Manufacturing Deployment 

a. Relative Cost 

(per year) 1 

b. Probability of 

Advancing N/A 

c. Duration 

(years) 

Table 3. Materials Innovation Process in the Improved Environment (Improved Technology 

Infrastructure) 

Discovery/Design Development Manufacturing Deployment 

a. Relative Cost 

(per year) 

b. Probability of 

Advancing N/A 

c. Duration 

(years) 
Appendix — Interview Guide 

A-5

IV. IMPACTS ON NEW AND IMPROVED PRODUCTS 

1. In addition to the impacts on the development process discussed in Section III, would the 

improved infrastructure lead to new opportunities to provide value to your customers or reach 

new markets? 

If so, how? 

2. Broadly speaking, would these impacts be more or less important to your company than the R&D 

impacts discussed in Section III? 

Could you quantify the relative importance? 

[ ] More important. Enter a number greater than 1: ____________ 

[ ] Equally important. 

[ ] Less important. Enter a number between 0 and 1: ____________ 

V. COMPANY CHARACTERISTICS 

To help us aggregate responses and control for differences in company characteristics, please provide 

the following information for your company: 

1. Industry (NAICS) 

[ ] Food (311) [ ] Petroleum and Coal Products (324) 

[ ] Chemical (325) [ ] Plastics and Rubber Products (326) 

[ ] Primary Metals (331) [ ] Fabricated Metal Product (332) 

[ ] Machinery (333) [ ] Computer and Electronic Product (334) 

[ ] Electrical Equipment, Appliance, and Component (335) 

[ ] Transportation Equipment (336) 

[ ] Miscellaneous, incl. medical equipment and supplies (339) 

2. Company Size (Total Number of Employees) 

[ ] less than 50 

[ ] 50–99 

[ ] 100–249 

[ ] 250–499 

[ ] 500–999 

[ ] 1,000–4,999 

[ ] 5,000–9,999 

[ ] 10,000–24,999 

[ ] 25,000 or more 
Economic Analysis of National Needs for Technology Infrastructure 

to Support the Materials Genome Initiative 

A-6

V. COMPANY CHARACTERISTICS CONTINUED 

1. R&D Size (Total Number of R&D Employees) 

[ ] less than 5 

[ ] 5–9 

[ ] 10–24 

[ ] 25–49 

[ ] 50–99 

[ ] 100–499 

[ ] 500–999 

[ ] 1,000–2,499 

[ ] 2,500 or more 

2. What percentage of your company’s R&D effort is related to developing and applying advanced 

materials? 

0% to 20% 20% to 40% 40% to 60% 60% to 80% 80% to 100% 

[ ] [ ] [ ] [ ] [ ] 

3. On average, for companies in your industry, roughly what percentage of R&D effort do you think 

is related to developing and applying advanced materials? 

0% to 20% 20% to 40% 40% to 60% 60% to 80% 80% to 100% 

[ ] [ ] [ ] [ ] [ ] 

VI. CONCLUDING THOUGHTS 

Is there anything else that we have not covered that you think is important for us to know? 

Is there anyone else that you would recommend we reach out to for our analysis? 

Many thanks for your input!