Company Opportunities with the Materials Informatics Skunkworks

Dane MorganUniversity of Wisconsin, Madison

Department of Materials Science and Engineeringddmorgan@wisc.edu

W: 608-265-5879C: 608-234-2906

April 20, 2015

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What is Materials Informatics?

Materials informatics is a field of study that applies the tools and principles of information extraction from data (informatics) to materials science and engineering to better understand the use, selection, development, and discovery of materials.

– Mining for materials information in large data sets– Applying new information technologies to enable

new materials science

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Materials Informatics is Not New!

Mendeleev 1871

Ashby map3

Turning Point for Materials Informatics

Data availabilityData Production Informatics Tools

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Plan: An Undergraduate “Materials Informatics Skunkworks”

Establish ~10 undergraduates working together to provide materials informatics research for companies• Help companies develop and utilize this new field• Provide training in rapidly growing field of informatics to

undergraduates to enhance employment opportunities and key workforce development

• Be supported financially through credits, internships, senior design/capstone projects, funded projects from industry

• Be supported intellectually through group culture of teamwork and knowledge continuity (more senior train more junior members) with limited faculty involvement for advanced issues

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Focus Area: Machine Learning for Knowledge Discovery in Large Data Sets

Use machine learning techniques to • Organize your data by putting all relevant, cleaned

input and output into one place• Understand your data by finding the most

important factors controlling output values• Expand your data by interpolating and

extrapolating• Optimize your data by finding correlations between

input and output data to optimize desired output

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Example

• Organize: Build a database of all the relevant factors (impurity concentrations, processing conditions, testing conditions, …) and output performance.

• Understand: Which impurities matter most. Size of impurity effects vs. other contributions.

• Expand: Interpolate/extrapolate to other impurity concentrations to assess performance under conditions we have not yet explored.

• Optimize: Determine impurity concentrations that lead to optimal performance.

I know impurities impact my device lifetime, so …

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Example: Predicting Impurity Diffusion in FCC Alloys

• 15 FCC hosts x 100 impurities = 1500 systems, ~15m core-hours (~$500k to produce, ~2 years).

• We have computed values for ~10%

• How can we quickly (and cheaply) get to ~100% coverage?

UNPUBLISHED DATA – CONFIDENTIAL – DO NOT DISSEMINATE

Materials Informatics Approach – Regression and Prediction

• Assume Activation energy = F(elemental properties)• Elemental properties = melting temperature, bulk modulus,

electronegativity, …• F is determined using a one of many possible methods: linear

regression, neural network, decision tree, kernel ridge regression, …

• Fit F with calculated data, test it with cross-validation, then predict new data.

Train F(properties)

Y. Zeng and K. Bai, Journal of Alloys and Compounds 624, p. 201-209 (2015).9

Model Predictive Ability

• Leave one out cross validation

• Predictive RMS = 0.24 eV – predicts diffusion of new impurity within ~10x at 1000K

• Time to predict new system < 1s!

UNPUBLISHED DATA – CONFIDENTIAL – DO NOT DISSEMINATE10

Who Did the Work?Undergraduate Informatics Team!

Benjamin Anderson

Liam Witteman

Team guru and postdoc

Henry Wu

Aren Lorenson

Haotian Wu

Zachary Jensen

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What the Informatics Skunkworks Might Provide Companies

WORKFORCEA team of talented students who are ready to work quickly with

your company to get the most out of your data

DATA ANALYTICSTechnical skills to help you organize, understand and expand data

sets and utilize data to optimize materials development

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What Companies Might Provide the Informatics Skunkworks

FINANCIAL SUPPORTInternships, Senior design/Capstone projects, Research projects

with Faculty+Skunkworks Teams

SHARED DATAData sets of materials related performance and property data that are large (> ~50), can be shared (ideally published), and are worth

mining

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Thank You for Your Attention

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