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Meeting MS&T21: Materials Science & Technology
Symposium Ceramics and Glasses Modeling by Simulations and Machine Learning
Presentation Title Kinetic Monte Carlo Simulation of Glasses Aided by Machine Learning
Author(s) Ajay Annamareddy
On-Site Speaker (Planned) Ajay Annamareddy
Abstract Scope Studying the kinetics in the glassy state, especially of an aged glass, becomes computationally prohibitive with molecular dynamics simulations because of the slowdown in dynamics. We aim to integrate kinetic Monte Carlo with machine learning (ML) to study the dynamics and diffusion in the aged state. This requires that ML methods be trained to accurately predict the hop-rate and hop-vector from a local description of atom. The hop-rate is accessible from softness developed by Andrea Liu and collaborators. Initial studies on how much hop-vectors can vary given the same environment indicates that the same starting conditions lead to hops clustered closely together in direction, supporting that hop-vector is approximately controlled by local environment and can be machine-learned. To predict the hop-vector, we adopted existing schemes used in ML interatomic potentials to define a local coordinate system and a rotationally-invariant feature vector and now using a deep learning convolutional neural network.
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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