报告摘要：
We introduce a series of deep learning based methods for molecular modeling at different scales. First, we introduce the Deep Potential scheme based on a many-body potential and interatomic forces generated by a carefully crafted deep neural network trained with ab initio data[1-3]. We show that the proposed scheme provides an efficient and accurate protocol for a variety of systems, especially some challenging materials systems like the high-entropy alloy. Next, we show how this scheme is generalized for simulating coarse-grained systems[4]. Finally, we present a new scheme called reinforced dynamics for enhanced sampling and efficient learning[5]. We shall also highlight the DeePMD-kit package that we developed for wide applications in computational physics, chemistry, biology, and materials science[6].
 
References:
[1] Jequn Han, Linfeng Zhang, Roberto Car, and Weinan E, "Deep potential: a general representation of a many-body potential energy surface." Communications in Computational Physics 23.3 (2018): 629-639.
[2] Linfeng Zhang, Jequn Han, Han Wang, Roberto Car, and Weinan E, "Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics." Physical Review Letters 120 (2018): 143001.
[3] Linfeng Zhang, Jiequn Han, Han Wang, Wissam A. Saidi, Roberto Car, and Weinan E, "End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems." arXiv: 1805.09003.
[4] Linfeng Zhang, Jequn Han, Han Wang, Roberto Car, and Weinan E, "DeePCG: constructing coarse-grained models via deep neural networks." Accepted by J Chem. Phys, arXiv:1802.08549 (2018).
[5] Linfeng Zhang, Han Wang, and Weinan E. "Reinforced dynamics for enhanced sampling in large atomic and molecular systems." The Journal of chemical physics 148.12 (2018): 124113.
[6] Han Wang, Linfeng Zhang, Jiequn Han, and Weinan E, "DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics." Computer Physics Communications, 2018: 0010-4655. (codes:https://github.com/deepmodeling/deepmd-kit)
 
报告人简介：
Linfeng Zhang graduated from Yuanpei College, Peking University in 2016. He is now a graduate student in the Program in Applied and Computational Mathematics (PACM), Princeton University, working with Profs. Roberto Car and Weinan E. Linfeng is interested in various mathematical and physical problems originated from different disciplines of sciences. Most recently Linfeng has been focusing on developing a deep learning based general purpose inter-atomic potential energy model for molecular and materials systems.