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Meeting MS&T21: Materials Science & Technology
Symposium Ceramics and Glasses Modeling by Simulations and Machine Learning
Presentation Title P1-3: Molecular Dynamic Characteristic Temperatures for Predicting Metallic Glass Forming Ability
Author(s) Lane Enrique Schultz, Dane Morgan, Izabela Szlufarska, Benjamin Afflerbach
On-Site Speaker (Planned) Lane Enrique Schultz
Abstract Scope 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.
Proceedings Inclusion? Undecided

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Machine-learning Based Hierarchical Framework to Discover Novel Scintillator Chemistries
Bayesian Optimization of Silicon Nitride Empirical Potentials
Ceramics from Polymers –– Results of Ab Initio Molecular Dynamic Simulations
Deciphering the Viscosity of Glass Materials with Machine Learning
Decomposing the Strength of Hydrated Cement Compositions by Machine Learning
Development of a Reactive Force Field (ReaxFF) for Simulation of Polymer-derived Ceramics
Development of a Transferable Inter-atomic Potential for Boroaluminosilicate Glasses
Effect of Polydispersity on the Fracture Properties of Calcium–Silicate–Hydrate Gel
Elucidating Compositional Governance of Optical Properties of Oxide Glasses Using Interpretable Machine Learning
Fusing Experimental and Simulation Datasets in Machine Learning for Predicting Glass Properties
Graph ODE for Learning Dynamic Systems
Impact of Irradiation on the Properties of Gel Layer Formed After Aqueous Corrosion of Borosilicate Glasses
Kinetic Monte Carlo Simulation of Glasses Aided by Machine Learning
Looking for Order in Disorder: Topological Data Analysis of Glass Structure
Machine Learning as a Tool to Accelerate the Design of Nuclear Waste Glasses with Enhanced Sulfur Loadings
Modeling Polaron Hopping in Ternary Spinel Oxides
Now On-Demand Only: Information Extraction Pipeline for Glasses: An NLP Based Approach
P1-3: Molecular Dynamic Characteristic Temperatures for Predicting Metallic Glass Forming Ability
The Energy Landscape Governs Ductility in Disordered Materials
Toward Revealing Full Atomic Picture of Nanoindentation Deformation Mechanisms in Li2O-2SiO2 Glass-ceramics

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