Machine learning for novel and improved inorganic scintillators

Scintillators are detector materials with a wide range of applications, from medical imaging to radiation detection. These materials convert a fraction of the energy deposited by high energy radiation into visible or ultraviolet photons. An ideal scintillator may have high light output, fast response time, and emission at suitable wavelengths. However, no single scintillator is ideal for all uses; there is a need to design custom scintillators optimized for each application. Currently, the discovery and design of new scintillators relies on a laborious, time-intensive approach, yielding little physical insight and leaving a vast space of potentially revolutionary materials unexplored. To accelerate the discovery of optimal scintillator materials, we are developing a closed loop machine learning driven adaptive design framework based on literature data, experiments and calculations. This talk presents an overview of this framework, focusing on the screening of complex chemistries with high band-gaps to identify promising materials amenable to band-gap/band-edge engineering to yield custom scintillation properties. The framework is general and is expected to prove useful for applications beyond scintillator discovery such as photovoltaic and semi-conductor materials.