【学术视频】机器学习-计算化学Workshop | 复旦大学刘智攀教授
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图 | 刘智攀
题 目：Global optimization based Neural Network Potential and Reaction Prediction Artificial Intelligence
报告人：刘智攀单 位：复旦大学时 间：2019-09-04地 点：厦门大学化学化工学院扫码观看精彩报告视频
报告摘要
While the underlying potential energy surface (PES) determines the structure and other properties of material, it has been frustrated to predict new materials from theory even with the advent of supercomputing facilities. The accuracy of PES and the efficiency of PES sampling are two major bottlenecks, not least because of the great complexity of material PES. This lecture introduces a “Global-to-Global” approach for material discovery by combining for the first time the global optimization method with neural network (NN) techniques. The novel global optimization method, the stochastic surface walking (SSW) method is carried out massively in parallel for generating a global training data set, the fitting of which by the atom-centered NN produces a multi-dimensional global PES; the subsequent SSW exploration of large systems with the analytic NN PES can provide key information on the thermodynamics and kinetics stability of unknown phases identified from global PES. We describe in detail the current implementation of the SSW-NN method with particular focuses on the size of the global data set and the simultaneous energy/force/stress NN training procedure. All these methods have been implemented in LASP software (www.lasphub.com). A number of important functional materials, in particular those for catalysis e.g. ZnCrO oxides, are utilized as the examples to demonstrate the automated global data set generation, the improved NN training procedure and the application in material discovery and catalysis. As a general tool for material simulation, the SSW-NN method provides an efficient and predictive platform for large-scale computational material screening.个人简介
刘智攀，复旦大学教授、博导。1997年本科毕业于上海交通大学，2003年博士毕业于英国女王大学，2003-2005年在英国剑桥大学从事表面科学理论研究。主要从事理论计算化学方法发展和表界面化学反应过程的计算模拟。在复杂势能面搜寻方法发展，全局神经网络势函数构建，固液界面电化学催化，多组分复相催化基础理论等领域，取得了系列重要成果，建立了一套新颖高效的理论催化研究框架，推进了理论催化化学发展。主持科技部重点研发纳米科技项目,国家自然科学基金重点课题等多项国家级项目课题。已经发表SCI收录论文150余篇，总引用数7000多次。曾获得长江学者和杰出青年基金资助，国际化学与应用化学学会（IUPAC）青年化学家奖。现任J. Phys. Chem. A/B/C 高级编辑。
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。
●【学术视频】机器学习-计算化学Workshop | 麻省理工学院谢天：Tutorials for CGCNN and GDyNets
●【学术视频】机器学习-计算化学Workshop | 上海大学欧阳润海研究员：Data-Driven Materials Discovery with the Method SISSO
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