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Meeting MS&T21: Materials Science & Technology
Symposium Ceramics and Glasses Modeling by Simulations and Machine Learning
Presentation Title Looking for Order in Disorder: Topological Data Analysis of Glass Structure
Author(s) Morten M. Smedskjaer, Søren S. Sørensen, Christophe A. N. Biscio, Lisbeth Fajstrup, Mathieu Bauchy
On-Site Speaker (Planned) Morten M. Smedskjaer
Abstract Scope An important question to unravel within materials science is the interplay between structure and properties in glass materials. To understand this link, there has been a great interest in pinpointing structural features that correlate strongly with the properties. However, identifying such structural descriptors especially at the medium-range length scale remains a challenging task. In this talk, we present our work on using topological data analysis to reveal hidden medium-range order (MRO) in oxide and hybrid glasses. Specifically, we apply persistent homology, a type of topological data analysis, to categorize and understand MRO structure in these systems, for which the atomic configurations have been generated by molecular dynamics simulations. By using persistent homology to study the size of certain algebraic topological features, we observe similarities to the length scales associated with the well-known first sharp diffraction peak in the studied glasses.
Proceedings Inclusion? Undecided

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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