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Meeting MS&T22: 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 05/15/2022
Proceedings Plan Undecided
PRESENTATIONS APPROVED FOR THIS SYMPOSIUM INCLUDE

A Physics Informed Machine Learning Approach to Predict Glass Forming Ability
Data-driven Prediction of Room Temperature Density of Multicomponent Silicate-based Glasses
Data Driven Design and Enhancement of Machinable Glass Ceramics
Developing Iron Phosphate glass models using ab-initio molecular dynamics simulations
Developing ReaxFF for Simulation of Silicon Carbonitride Polymer-derived Ceramics
Development of Structural Descriptors to Predict Dissolution Rate of Volcanic Glasses: Molecular Dynamic Simulations
In-Silico Simulations of Polymer Pyrolysis
Machine Learning-Derived Atomistic Potentials for Y2Si2O7 and Yb2Si2O7
Machine Learning Defect Properties of Semiconductors
Machine Learning to Design and Discover Sustainable Cementitious Binders: Learning from Small Databases and Developing Closed-form Analytical Models
Molecular Dynamic Simulations of Polymer Derived Ceramics
Molecular Dynamics Simulation of Tellurite Glasses
Molecular Dynamics Study of Domain Switching Dynamics in KNbO3 and BaTiO3
Natural Language Processing Aided Understanding of Material Science Literature
Pore-resolved Simulations of Chemical Vapor Infiltration in 3D Printed Preforms and the Kinetic Regimes
Predicting and Accessing Metastable Phases
Predicting the Dynamics of Atoms in Glass-Forming Liquids by a Surrogate Machine-Learned Simulator
Quantifying the Local Structure of Metallic Glass as a Function of Composition and Atomic Size
Using Machine Learning Empirical Potentials to Investigate Interdiffusion at Metal-Chalcogenide Alloy Interfaces


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