中国科学院理论物理研究所 中国科学院理论物理研究所
学术报告

学术活动

学术报告
03/22 2021
  • Title题目 (Seminar) Integration of molecular modeling, machine learning, and high performance computing
  • Speaker报告人
  • Date日期
  • Venue地点
    • Abstract摘要

    CAS Key Laboratory of Theoretical Physics

    Institute of Theoretical Physics

    Chinese Academy of Sciences

    Seminar

    Title

    题目

    		 Integration of molecular modeling, machine learning, and high performance computing

    Speaker

    报告人
    Linfeng Zhang

    Linfeng Zhang is temporarily working as a research scientist at the Beijing Institute of Big Data Research. In the May of 2020, he graduated from the Program in Applied and Computational Mathematics (PACM), Princeton University, working with Profs. Roberto Car and Weinan E. Linfeng has been focusing on developing machine learning based physical models for electronic structures, molecular dynamics, as well as enhanced sampling. He is one of the main developers of DeePMD-kit, a very popular deep learning based open-source software for molecular simulation in physics, chemistry, and materials science.He is a recipient of the 2020 ACM Gordon Bell Prizefor their project “Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning”.

    Affiliation

    所在单位

    		 Beijing Institute of Big Data Research

    Date

    日期

    		 2021年3月22日15:00-16:00

    Venue

    地点

    		 6620

    Contact Person

    所内联系人

    		 张潘

    Abstract

    摘要

    In this talk, I will present several theories, methods, and engineering efforts that integrate physical models with machine learning and high-performance supercomputers, including learning assisted electronic structure models, learning assisted molecular dynamics models, as well as learning assisted enhanced sampling schemes.  Then I will present our efforts on developing related open-source software packages and high-performance computing schemes, which have now been widely used worldwide by experts and practitioners in the molecular and materials simulation community. Several important practical applications will be given as examples.

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