Skunworks Final poster 2015-09-21_eau_claire

Machine Learning For Predicting Missing Diffusion DataLiam Witteman*1,Ben Anderson**2, Haotian Wu2, Aren Lorenson2, Henry Wu2, Dane Morgan2

1Department of Chemical and Biological Engineering,1415 Engineering Drive, Madison, WI 53706, University of Wisconsin - Madison2Department of Material Science & Engineering, 1509 University Avenue, Madison, WI 53706, University of Wisconsin - Madison

*email: lwitteman@wisc.edu **email: bdanderson2@wisc.edu

Introduction:Diffusion coefficients tell us how atoms move under a driving force

MAterials Simulation Toolkit (MAST) is an automated high-throughput workflow manager for first-principles diffusion calculations

Diffusion is important in manufacturing items such as:

Data:To cover just FCC hosts: M(FCC)-X● Needs ~15m core-hours● Only covered ~10% so far

Question: How to quickly and cheaply calculate therest of the M-X dataspace?

Software Infrastructurefor sustained Innovation(Si2) award No. 1148011

Neural Network: Gaussian Kernel Ridge Regression

Conclusions:

● Idea is to mimic the brain● Great at finding trends and

patterns

● Idea is to move to a higher dimensionality where a linear trend can be found

Results:

Assessment of “smart consensus method” using RMS in 20% left out of dataset (5 test data “cases”).

With a root-mean-square of less than 300 meV, both machine learning tools have predictive capabilities that can provide useful predictions of missing data and speed completion of the Dataspace. Further validation of prediction error is underway.

Best and Worst fits of Leave Out 20% Test, Average RMS: 179 meVBest: 118 meV Worst: 265 meV

References:● www.mydailynew.com● www.indusoft.com● en.wikipedia.org/wiki/Fuel_cell

With 7 host-impurity pairs, rms drops below 400 meV (300 on average)

-First principle diffusion data of impurity X in host M

● www.texample.net● http://www.eric-kim.net