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Meeting MS&T21: Materials Science & Technology
Symposium Ceramics and Glasses Modeling by Simulations and Machine Learning
Sponsorship ACerS Glass & Optical Materials Division
Organizer(s) Mathieu Bauchy, University of California, Los Angeles
Peter Kroll, University of Texas at Arlington
N. M. Anoop Krishnan, Indian Institute of Technology Delhi
Scope This symposium will provide a forum to identify current achievements and existing challenges in the modeling of ceramic and glassy materials by simulation and machine learning. The symposium will span over various types of materials (ordered or disordered), lengthscales, and properties. Computational techniques of interest include: classical and ab initio molecular dynamics simulations, mesoscale simulations, continuum modeling, data mining, machine learning, natural language processing, optimization, etc. Contributions that beneficially combine physics-based simulations and machine learning (wherein one technique informs, advances, or replaces the other) are of special interest, but studies solely focusing on simulation or machine learning are also welcomed.

Topics include, but are not limited to:
• Informatics and machine learning to predict materials properties,
• Physics-informed machine learning,
• Data mining and automated data extraction from the literature,
• Interatomic forcefield development,
• Machine learning approaches to decode structure-property relationships,
• High-throughput simulations to generate big data,
• First-principle and classical modeling for structure and property prediction,
• Upscaling techniques and mesoscale modeling,
• Continuum modeling of glasses and ceramic materials,
• Meta-optimization and inverse design, and,
• Machine learning for image/microstructure analysis.

Abstracts Due 04/15/2021
Proceedings Plan Undecided

A Machine-learning Based Hierarchical Framework to Discover Novel Scintillator Chemistries
Application of Machine Learning to Novel Ceramic Material Discovery
Bayesian Optimization of Silicon Nitride Empirical Potentials
Ceramics from Polymers –– Results of Ab Initio Molecular Dynamic Simulations
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
Elemental DeepPot Neural Networks and Applications to Ceramics
Elucidating Compositional Governance of Optical Properties 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
Information Extraction Pipeline for Glasses: An NLP Based Approach
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
P1-3: Molecular Dynamic Characteristic Temperatures for Predicting Metallic Glass Forming Ability
P1-4: Deciphering the Viscosity of Glass Materials with Machine Learning
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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