Skunworks Final poster 2015-09-21_eau_claire
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Machine Learning For Predicting Missing Diffusion Data Liam Witteman* 1 ,Ben Anderson** 2 , Haotian Wu 2 , Aren Lorenson 2 , Henry Wu 2 , Dane Morgan 2 1 Department of Chemical and Biological Engineering,1415 Engineering Drive, Madison, WI 53706, University of Wisconsin - Madison 2 Department of Material Science & Engineering, 1509 University Avenue, Madison, WI 53706, University of Wisconsin - Madison *email: [email protected] **email: [email protected] 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 the rest of the M-X dataspace? Software Infrastructure for sustained Innovation (Si 2 ) 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 meV Best: 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
Transcript of 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: [email protected] **email: [email protected]
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
