AI for Materials Science: Tuning Laser-Induced Graphene Production - - PowerPoint PPT Presentation
AI for Materials Science: Tuning Laser-Induced Graphene Production Lars Kotthofg, Vivek Jain, Alexander Tyrrell, Hud Wahab, and Patrick Johnson Artifjcially Intelligent Manufacturing Center larsko@uwyo.edu Data Science meets Optimization
Lars Kotthofg, Vivek Jain, Alexander Tyrrell, Hud Wahab, and Patrick Johnson
Artifjcially Intelligent Manufacturing Center larsko@uwyo.edu Data Science meets Optimization Workshop, 11 August 2019
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▷ treat tunable process as black box – no knowledge of inner workings required ▷ intelligently and iteratively select parameter settings likely to improve performance ▷ mature techniques used in many areas of AI
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▷ reduce graphene oxide to graphene through laser irradiation ▷ allows to create electrically conductive lines in insulating material ▷ laser parameters need to be tuned carefully to achieve good results
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Iteration Ratio
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4 6 2 4 6 8
Iteration Ratio
During Training After 1st prediction + Prediction
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▷ improvement of factor of two over best result in literature ▷ good results even with small amount of initial data (19 evaluations) ▷ code can be used by domain experts with no background in machine learning
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4 3 45 5 7 6 8 2 15 13 14 12 17 26 21 22 27 19 20 18 25 9 24 31 40 32 35 29 38 41 34 33 11 30 28 10 16 23 46 36 42 37 39 47 48 44 43 2 4 6
Parameter Space Ratio
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▷ application to other materials ▷ more in-depth investigation of Bayesian Optimization performance ▷ inform understanding of process by what surrogate model has learned
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4 6 2 4 6 8
Iteration Ratio
During Training After 1st prediction + Prediction
50 um 50 um 12