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Meeting MS&T23: Materials Science & Technology
Symposium Ceramics and Glasses Modeling by Simulations and Machine Learning
Presentation Title Decoding the Structural Genome of Silicate Glasses
Author(s) Qi Zhou
On-Site Speaker (Planned) Qi Zhou
Abstract Scope Silicate glasses exhibit a wide range of properties. To understand, tune, and enhance glasses’ properties, one needs to decode the “glass genome,”, to uncover how basic structural features control glass’ macroscopic properties—as DNA governs a given individual’s characteristics. This requires as a prerequisite the accurate knowledge of the atomic structure of silicate glasses.Experiments typically offer indirect signatures of the three-dimensional atomic structure of glasses. Although molecular dynamics simulations offer direct access to glasses’ structure, they come with their shortcomings. We present force-enhanced atomic refinement as a powerful modeling technique to unveil the 3D structure of glasses. We demonstrate that FEAR yields glass structures that exhibit improved thermodynamic stability compared to MD, but an unmatched level of agreement with experimental data. We show how with FEAR allows us to solve several puzzles in glass science, including how the atomic structure of glasses governs their response to changes in temperature and pressure.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

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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