报告题目：AI in materials science
报告人：Prof. Turab Lookman
报告时间：2024年6月24日周一15:00
报告地点：宝山校区D507
Abstract:
Materials informatics is an emerging area fusing aspects of computer science and machine learning with statistical inference and materials science. I will start by briefly providing a historical perspective on the value of information in the sciences and review some of the early work in materials science using older versions of tools we use today. Guiding materials discovery is essentially making smart or optimal decisions of what experiments or calculations to do next so that one can minimize the number needed to discover materials with desired properties. I will show how algorithms employed to train alphaGO, that has beaten GO grandmasters, are beginning to help us find materials with target response. Finally, in contrast to merely learning from data, I will show how physics-based models can be employed to guide the discovery process.
Bio：
Turab Lookman obtained his Ph.D. from Kings College, University of London, and held university appointments at Western University and the University of Toronto in Canada until 1999. He was elected Fellow of the American Physical Society (APS) in 2012 and a Laboratory Fellow at Los Alamos National Laboratory in 2018. His interests and expertise lie in hard and soft materials science and condensed matter physics, applied mathematics, and computational methods. His work on information directed approaches to materials discovery started in 2012 when he was funded by LANL/DOE to investigate how ML tools could be applied to accelerate materials discovery. Their work led to applying experimental design methods, such as Bayesian Global Optimization, within an active learning frame work to find materials with targeted response. He has published over 450 papers, has 15.6K citations with an h index of 61 (Google Scholar).
欢迎学校广大师生共同聆听此次讲座！