MateriAlZ Seminar: Ghanshyam Pilania
Friday, February 25, 2022, 11:00 a.m. MST
Dr. Ghanshyam Pilania
Scientist, Materials Science and Technology Division
Los Alamos National Laboratory
"Integrating Experiments, Simulations and Machine Learning for Novel Scintillator Discovery"
Zoom Link | Passcode: 006253
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Abstract
Inorganic scintillator-based detector materials find a wide variety of applications, ranging from medical imaging to radiation detection for global security and high energy physics experiments. These materials essentially convert a fraction of the total energy deposited by incident gamma rays or X-rays into a 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; there is a need to design custom scintillators optimized for each application. Currently, the discovery and design of new detector materials rely 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 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 the literature, in-house experiments and quantum mechanical calculations. This talk will present an overview of this framework, focusing on the screening of complex chemistries (such as perovskites, garnets and elpasolites) with wide band-gaps to identify promising materials that are amenable to band-gap/band-edge engineering to yield custom scintillation properties. The developed framework is general and is expected to prove useful in identifying candidates for applications beyond scintillator discovery such as photovoltaics and catalysis.
Bio
Dr. Ghanshyam Pilania works as a scientist in the Materials Science and Technology Division at the Los Alamos National Laboratory. He received his Ph.D. in Materials Science & Engineering from the University of Connecticut, Storrs in 2012. His postdoctoral research was supported by an Alexander von Humboldt fellowship (at the Fritz Haber Institute of the Max Planck Society in Berlin, Germany) and a distinguished Director’s Postdoctoral Fellowship at the Laboratory. He specializes in developing and applying high-throughput electronic structure and atomistic methods to understand and design functional materials, with a particular focus on targeted materials design and discovery utilizing machine learning and materials informatics.