报告题目“Machine learned force fields: status and challenges”

报告时间：2020/12/17 16:45

地点：创新大厦1330

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报告人：Garbor Csanyi教授

报告摘要：I will make the somewhat bold claim that over the past 10 years, a new computational task has been defined and solved for extended material systems: this is the analytic fitting of the Born-Oppenheimer potential energy surface as a function of nuclear coordinates under the assumption of medium-range interactions, out to 5-10 Å. The resulting potentials are reactive, many-body, reach accuracies of a few meV/atom, with costs that are on the order of 1-10 ms/atom. Important challenges remain: treatment of long range interactions in a nontrivial way, e.g. environment dependent multipoles, charge transfer, magnetism. Time is ripe for a“shakedown”of the details among various approaches (neural networks, kernels, polynomials), and more standard protocols of putting together the training data. Tradeoffs between system- (or even project-) specific fits vs. more general potentials will be ongoing. I am particularly concerned with the amount physics and chemistry that we impute into these approximations, and they can be used to help "extrapolate" correctly into regions of configuration space far from those in the data set.

报告人简介：Garbor Csanyi，剑桥大学工程系教授，主要研究方向包括原子尺度分子动力学模拟、量子力学与经典力学耦合方法及多体相互作用的研究。Csanyi教授已经在相关领域有很多高水平和高影响力的工作，例如他提出的Gaussian-appoximation-potential (GAP)机器学习势已经被广泛接受，成为目前机器学习在融合物理材料模拟中的一个成功范例。他与合作者设计开发的计算程序QUIP是分子动力学模拟的一个重要工具，已经被很多研究学者使用，并成功应用在C、Si、W、H20等体系的模拟。此外，Csanyi教授在机器学习势框架下提出的SOAP和bispectrum discriptor描述符也被很多机器学习材料结构研究专家所广泛引用。Garbor Csanyi教授在结构搜索算法开发方面也有颇深研究和见解，与国内相关课题组保持着密切合作。