Applications of Machine Learning to Materials Modeling
||Prof. Efthimios Kaxiras|
Department of Physics and School of Engineering and Applied Sciences, Harvard University
||Exhibition Hall, Hefei National Laboratory Building|
The last few years have witnessed a surge of activity in machine learning approaches applied to materials science. In this talk I will address both the promise and the limitations of using data science ideas to explore the possibilities of “materials by design”, drawing on examples from recent research in our group. Applications of our work focus on exploring the properties of new materials for energy related problems, including improved batteries, photovoltaics, and new catalysts; in a parallel but distinct type of approach, we have been exploring how machine learning approaches can shed light into fundamental questions like the strength of amorphous solids.
Dr. Kaxiras received a PhD in theoretical condensed matter physics from MIT and joined the faculty of Harvard University in 1991. He is the Founding Director of the Institute for Applied Computational Science, served as the Director of the Initiative on Innovative Computing, and his distinctions include Fellow of the American Physical Society and Chartered Physicist of the Institute of Physics. His research interests encompass a wide range of topics in the physics of solids and fluids, most recently on materials for renewable energy, especially batteries and photovoltaics, and on simulations of blood flow in coronary arteries.
||Hefei National Laboratory for Physical Sciences at the Microscale|
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This article came from News Center of USTC.