报告题目：What is the target of a machine-learned potential energy surface? How the answer informs the approach taken
报告人：Prof. Joel M. Bowman, Emory University
时间：2021年10月22日09:00
地点：曾呈奎楼3楼B311
报告摘要：
There has been dramatic progress in developing so-called “machine-learned potential energy surfaces”. After going the basics of what this means and also a quick survey of the ML methods currently employed my talk will emphasize that potential energy surfaces are of course a means to an end. Namely, to high-quality computational chemistry ranging from reaction dynamics, spectroscopy, properties of clusters, hydrate clathrates and the condensed phase. The “target” of the potential is the science of interest and more specifically whether the approach is quantum or classical or semi-quantum/semi-classical. I will illustrate this with a number of specific case studies ranging from the tunneling splitting in malonaldehyde and the formic acid dimer to the conformations of glycine which include consideration of rigorous zero-point motion.
报告人简介：
Joel M. Bowman于1986年任埃默里大学教授，2010年到牛津大学做访问学者，2001年到耶鲁大学做范围教授，1994年到JILA做访问学者，2003-2006年和1990-1993年担任埃默里大学化学系主任，1983-1984年到芝加哥大学做访问教授，1982-1986年在伊利诺理工学院做教授，1977-1982年在伊利诺理工学院做副教授，1974-1977年在伊利诺理工学院做助理教授，1974年获得加州理工学院博士学位，发表论文500余篇，2020年到2021年8月选取的18篇文章的Google H-index是86，引用约为30000 。
