Materials, their surfaces, and interfaces are at the heart of modern technology, playing a central role in various fields such as biosensing, energy conversion, quantum computing, and nanoelectronics. Recent advancements in ab initio methods, data science, and artificial intelligence have dramatically expanded our ability to understand existing materials and discover new ones with greater ease, scope, and speed. In this talk, I will demonstrate how we have utilized a combination of ab initio simulations, high-throughput computations, and machine learning to discover and design materials, with a focus on ultra-wide bandgap materials and solar-energy materials.
Arunima K. Singh is an Associate Professor of Physics and a graduate faculty member in Materials Science and Engineering at Arizona State University. She received her Ph.D. from Cornell University in 2014 and held postdoctoral appointments at Lawrence Berkeley National Laboratory and the National Institute of Standards and Technology. Prof. Singh is a recipient of the Department of Energy Early Career Research Program Award and the Department of Defense Faculty Fellowship. Her research integrates first-principles computational methods and artificial intelligence to accelerate materials discovery, synthesis, and applications, with an emphasis on understanding and controlling physical phenomena at the surfaces and interfaces of materials.