报告题目：Integration of molecular modeling, machine learning and high performance computing
报告人：张林峰研究员, Beijing Institute of Big Data Research
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.
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”.