About this Abstract |
Meeting |
2020 TMS Annual Meeting & Exhibition
|
Symposium
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Computational Discovery and Design of Emerging Materials
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Presentation Title |
Machine-learning based Discovery of Novel Scintillator Chemistries |
Author(s) |
Anjana Anu Talapatra, Blas Uberuaga, Chris Stanek, Ghanshyam Pilania |
On-Site Speaker (Planned) |
Anjana Anu Talapatra |
Abstract Scope |
Perovskites are among the most common compounds and are employed in electronics, photonics, and energy detection technology. Their versatility stems from their propensity to accommodate a very large number of elemental combinations. This large compositional space naturally lends itself to modern learning paradigms to discover new and improved perovskites. Of special interest are double-halide-perovskites with mixed anion chemistries, which are known to be efficient scintillators. Scintillators have wide-ranging applications from medical imaging to global security. Despite a pressing need for improved scintillators for these diverse applications, the discovery and design of new scintillator materials has historically relied on laborious, time-intensive, trial-and-error approaches yielding little physical insight and leaving a vast space of potentially revolutionary materials unexplored. This talk presents an adaptive design framework coupling high-throughput experiments, first-principles computations and machine learning to efficiently screen a large chemical space of potential scintillator chemistries to identify materials with better scintillation properties. |
Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |