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Meeting MS&T21: Materials Science & Technology
Symposium Ceramics and Glasses Modeling by Simulations and Machine Learning
Presentation Title A Machine-learning Based Hierarchical Framework to Discover Novel Scintillator Chemistries
Author(s) Anjana Anu Talapatra, Blas Uberuaga, Christopher R. Stanek, Ghanshyam Pilania
On-Site Speaker (Planned) Anjana Anu Talapatra
Abstract Scope Scintillators are fascinating materials with wide-ranging applications. However, the discovery of new scintillator materials has traditionally relied on a laborious, time-intensive, trial-and-error approach, leaving a vast space of potentially revolutionary materials unexplored. To accelerate the discovery of optimal scintillators with targeted properties and performance, we present an adaptive design framework that couples density functional theory (DFT) computations and machine learning (ML) to (1) screen a large chemical space of potential chemistries and (2) identify promising chemistries via iterative inputs from theory and experiments. This talk will focus on the details of the screening strategy applied to the class of oxide perovskites as candidate scintillator materials. Specifically, we present a novel hierarchical down-selection approach that employs structure maps, DFT-based stability analysis, ML models for bandgap predictions and physics-based classification to efficiently predict minimal favorable electronic structure for a viable scintillator. The developed framework is general and has implications beyond scintillator discovery.
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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