【学术视频】机器学习-计算化学Workshop | 剑桥大学Gábor Csányi教授

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图 | Gábor Csányi

题   目：Advances in interatomic potentials for materials

报告人：Gábor Csányi

单   位：Engineering Laboratory University of Cambridge

时   间：2019-09-04

地   点：厦门大学化学化工学院

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报告摘要

Modelling the atomic scale properties of materials is one of the success stories of materials modelling over the past four decades. Increasingly complex functional forms, from pair potentials to embedded atom models and bond order potentials, allowed the quantitative description of different crystal structures, point and line defects, surfaces, shedding light on many elementary processes governing failure, phase stability, etc. Interestingly, the accuracy with which these models describe the potential energy surface corresponding to the electronic ground state has not changed over the decades and is rather limited. The success is thus largely empirical in nature - and follows from the sophistication of the modeller and the judicious compromises made in order to solve specific problems. The parallel developments in electronic structure theory on the other hand provided exquisite quantitative agreement with experiments e.g. for thermomechanical properties, phase stability, and defect energetics. I will report on recent work of a growing community, who have managed to bring these two worlds together, and construct extremely accurate functional representations of the interatomic potential. These developments rely on a very large amount of highly accurate electronic structure data, on non-parametric function fitting, and on sophisticated representation theory that brings with it guarantees of completeness and convergence.  

个人简介

Gábor Csányi got his PhD in computational physics, Massachusetts Institute of Technology 2001. He became Professor of Molecular Modelling in Department of Engineering at Cambridge in 2016. He is an expert in atomistic simulation, particularly in multi scale modelling that couples quantum mechanics to larger length scales. He is currently engaged in applying machine learning techniques to materials modelling problems e.g. deriving force fields (interatomic potentials) from ab initio data. He is also interested in statistical problems in molecular dynamics, e.g. in enhanced sampling algorithms that can be used to automate the calculation of phase diagrams from atomistic models. Some of the modelling work may have applications in robust optimization, and also in understanding materials failure. 

会议简介

2019年9月3日-6日，由固体表面物理化学国家重点实验室（厦门大学）、福建省理论与计算化学重点实验室和厦门大学化学化工学院主办的“机器学习-计算化学Workshop”在厦门大学化学化工学院举办。本次Workshop邀请了相关领域的研究者报告领域前沿进展，并设置Hands-on tutorials环节帮助学员们熟悉代码的使用。此次Workshop的举办增进了不同领域研究者的交流，促进了开源共享的观念传递，希望推动大数据技术在计算化学和材料模拟等领域的应用。

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