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(Seminar) Integration of molecular modeling, machine learning, and high performance computing
2021-03-22     Text Size:  A

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 Prize for 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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