About this Abstract |
Meeting |
2021 TMS Annual Meeting & Exhibition
|
Symposium
|
Accelerated Discovery and Qualification of Nuclear Materials for Energy Applications
|
Presentation Title |
Machine Learning Perovskites in the Quest for Improved Scintillators |
Author(s) |
Anjana Talapatra, Christopher R. Stanek, Blas P. Uberuaga, Ghanshyam Pilania |
On-Site Speaker (Planned) |
Ghanshyam Pilania |
Abstract Scope |
Inorganic scintillator-based detector materials find a wide variety of applications. These materials essentially convert a fraction of the total energy deposited by incident gamma rays or X-rays into visible or near-visible range of the spectrum. A "good" scintillator would exhibit high light output, fast response time, and emission at suitable wavelengths, among many other application-specific desired characteristics. However, no single scintillator is ideal for all uses. To accelerate the discovery of customized optimal scintillator materials with targeted properties and performance, efforts are ongoing to develop a closed-loop machine learning driven adaptive design framework based on data from literature, in-house experiments and quantum mechanical calculations. This talk will present an overview of this framework, focusing on the screening of complex perovskite and double perovskite chemistries with high band-gaps and other favorable electronic structure features to yield custom scintillation properties. |
Proceedings Inclusion? |
Planned: |