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Ceramics and Glasses Modeling by Simulations and Machine Learning: Poster Session
Sponsored by: ACerS Glass & Optical Materials Division
Program Organizers: Mathieu Bauchy, University of California, Los Angeles; Peter Kroll, University of Texas at Arlington; N. M. Anoop Krishnan, Indian Institute of Technology Delhi

Tuesday 11:00 AM
October 19, 2021
Room: Exhibit Hall B
Location: Greater Columbus Convention Center

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Poster
P1-3: Molecular Dynamic Characteristic Temperatures for Predicting Metallic Glass Forming Ability: Lane Schultz¹; Dane Morgan¹; Izabela Szlufarska¹; Benjamin Afflerbach¹; ¹University of Wisconsin-Madison
    We explore the use of characteristics temperatures derived from molecular dynamics to predict aspects of metallic Glass Forming Ability (GFA). We use temperatures derived from cooling curves of self-diffusion, viscosity, and energy as features for machine learning models of GFA. The GFA of alloys was quantified by melt-spinning or suction casting amorphization behavior, with alloys that showed crystalline phases after synthesis classified as Poor GFA and those with pure amorphous phases as Good GFA. This binary GFA classification was then modeled using decision tree-based methods and were assessed with nested-cross validation. The maximum F1 score for the precision-recall with Good Glass Forming Ability as the positive class was 0.82 ± 0.01 for the best model type. Although the predictive ability of the models developed here are modest, this work demonstrates clearly that one can use molecular dynamics simulations and machine learning to predict metal glass forming ability.

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