Machine Learning for the Materials World
Abstract: The last few years have witnessed a surge of activity in machine learning approaches applied to materials science, boosted in part by President Obama’s Materials Genome Initiative. While there is great promise, there are also pitfalls in applying data science and machine learning methods to the discovery of new materials. In this talk I will address both the promise and the pitfalls on using data science ideas to explore the possibilities of “materials by design”, drawing on examples from our recent research. Applications of our work focus on 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.
Bio: Professor 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, with recent emphasis on materials for renewable energy, especially batteries and photovoltaics, and on simulations of blood flow in coronary arteries.
All seminars are held on Wednesdays from 12:00 noon-1:00 p.m. in the Bowen Hall Auditorium Room 222. A light lunch is provided at 11:30 a.m. in the Bowen Hall Atrium immediately prior to the seminar.