Conference Tools for 2021 TMS Annual Meeting & Exhibition

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Meeting	2021 TMS Annual Meeting & Exhibition
Symposium	AI/Data Informatics: Applications and Uncertainty Quantification at Atomistics and Mesoscales
Presentation Title	Machine Learning Guided Discovery of Novel Oxide Perovskites for Scintillator Applications
Author(s)	Anjana Anu Talapatra, Blas Uberuaga, Christopher R Stanek, Ghanshyam Pilania
On-Site Speaker (Planned)	Anjana Anu Talapatra
Abstract Scope	Scintillators have wide-ranging applications, from medical imaging to radiation. Despite a pressing need for improved scintillators, the discovery of new scintillators relies on a laborious, time-intensive, trial-and-error approach; yielding little physical insight and leaving a vast space of potentially revolutionary materials unexplored. To accelerate the discovery of optimal scintillators, we are developing an adaptive design framework that couples high-throughput experiments, first-principles computations and machine learning to (1) screen a large chemical space of probable scintillator chemistries and (2) identify chemistries enabling further tuning of the underlying electronic structure for band edge and defect engineering. This talk focuses on the details of the screening strategy applied to the class of single and double oxide perovskites. Specifically, we present a novel hierarchical down-selection approach that employs non-traditional structure maps, DFT-based stability analysis, machine learning models for bandgap predictions and physics-based classification to efficiently predict minimal favorable electronic structure for a viable scintillator.
Proceedings Inclusion?	Planned:
Keywords	Machine Learning,

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