Abstract:

We identify a list of problems in the context of multi-scale molecular modeling and review machine learning (ML)-based strategies that boost simulations with ab initio accuracy to much larger scales than conventional approaches. Using examples at scales of density functional theory and molecular dynamics, we present two equally important principles: 1) ML-based models should respect important physical constraints in a faithful and adaptive way; 2) to build truly reliable models, efficient algorithms are needed to explore relevant physical space and construct optimal training data sets. Finally, we present our efforts on developing related open-source software packages and high-performance computing schemes.

Bio:

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 popular deep learning based open-source software for molecular simulation. 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”.