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Meeting MS&T23: 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,
• Inverse design, meta-optimization, and generative approaches,
• 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, and,
• Machine learning for image/microstructure analysis.

Abstracts Due 05/08/2023

A B-C Story, Investigated by A.I. and CALPHAD
An ICME Approach for Short Fiber Reinforced Ceramic Matrix Composite via Direct Ink Writing
Atomistic Perspectives in Characterizing Crystalline Defect Formation in Amorphous Silicon Nitride
Combining Experimental and Simulation Datasets in Machine Learning for Glass Properties Prediction
Comparison of Core Level Chemical Shift in CH3NH3PbBr3 Perovskite Due to Surface Terminations and Orientations of CH3NH3 Ion
D-10: Unraveling the structure and mechanical properties ZIFs and its topological equivalents: Large scale simulations
D-9: Discrete Element Simulation of Delamination in Thermal Barrier Coating
Decoding the Structural Genome of Silicate Glasses
Defect Chemistry and Electrical Properties of Doped BaTiO3
Development of a Machine Learned Interatomic Potential for Shock Simulations of Boron Carbide
First-Principles Modeling of Thermodynamics and Kinetics of Thin-Film Tungsten Carbides
Fracture Resistance of Rare-earth Phosphates as Environmental Barrier Coatings under CMAS Corrosion
Generation of Spectral Neighbor Analysis Potentials for Alpha Boron and Comparison of the Results with the Angular Dependent Potential
Lithium Dopant and Surface Effects on the Band Gap of Calcium Hexaboride (CaB6) Using DFT Methods
Machine Learning Prediction of Heat Capacity for Solid Mixtures of Pseudo-binary Oxides
Using Deep Learning to Develop a Smart and Sustainable Cement Manufacturing Process

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