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MS&T21: Materials Science & Technology

Symposium

Ceramics and Glasses Modeling by Simulations and Machine Learning

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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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