|About this Abstract
||MS&T21: Materials Science & Technology
||Ceramics and Glasses Modeling by Simulations and Machine Learning
||A Machine-learning Based Hierarchical Framework to Discover Novel Scintillator Chemistries
||Anjana Anu Talapatra, Blas Uberuaga, Christopher R. Stanek, Ghanshyam Pilania
|On-Site Speaker (Planned)
||Anjana Anu Talapatra
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.