Contact Information:

Email: wang_han@iapcm.ac.cn

Tel: 010-61935161

Education:
Postdoctoral researcher, Freie Universität Berlin, Germany, 2011-2014

Ph.D., Computational mathematics, Peking University, P.R. China, 2006-2011

B.S., Computational mathematics, Peking University, P.R. China, 2002-2006

Research Areas:

Multiscale modeling and simulation.

Interested Research Areas in CAPT：

Molecular scale modeling and simulation in high energy density physics.

Machine learning based molecular modeling and simulation.

High performance optimization for the software in molecular simulation.

Grants and Awards：

2020 ACM Gordon Bell prize.

Selected Publications：

1. Weile Jia, Han Wang, Mohan Chen, Denghui Lu, Lin Lin, Roberto Car, Weinan E and Linfeng Zhang. Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning. SC'20: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 5 1-14 (2020).

2. Linfeng Zhang, De-Ye Lin, Han Wang*, Roberto Car and Weinan E*. Active learning of uniformly accurate interatomic potentials for materials simulation. Physical Review Materials, 3, 023804 (2019).

3. Linfeng Zhang, Han Wang*, Weinan E*. Reinforced dynamics for enhanced sampling in large atomic and molecular systems. The Journal of Chemical Physics, 148, 124113 (2018).

4. Linfeng Zhang, Jiequn Han, Han Wang*, Roberto Car, Weinan E*. Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics. Physical Review Letters, 120, 143001 (2018).

5. Han Wang*, Xingyu Gao and Jun Fang. Multiple Staggered Mesh Ewald: Boosting the Accuracy of the Smooth Particle Mesh Ewald Method. Journal of Chemical Theory and Computation, 12(11), 5596-5608 (2016).

Pubilications in past five years：

1. Denghui Lu, Han Wang, Mohan Chen, Lin Lin, Roberto Car, Weinan E, WeileJia, Linfeng Zhang. 86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy. Computer Physics Communications, 259, 107624 (2021).

2. Jinzhe Zeng, Linfeng Zhang*, Han Wang*, and Tong Zhu*. Exploring the Chemical Space of Linear Alkane Pyrolysis via Deep Potential GENerator. Energy Fuels, in press (2020).

3. Yixiao Chen, Linfeng Zhang*, Han Wang*, and Weinan E. DeePKS: A Comprehensive Data-Driven Approach toward Chemically Accurate Density Functional Theory. Journal of Chemical Theory and Computation, in press (2020).

4. Yixiao Chen, Linfeng Zhang, Han Wang, and Weinan E. Ground State Energy Functional with Hartree-Fock Efficiency and Chemical Accuracy. J. Phys. Chem. A, 124(35), 7155–7165 (2020).

5. Haidi Wang, Yuzhi Zhang, Linfeng Zhang* and Han Wang*. Crystal Structure Prediction of Binary Alloys via Deep Potential. Front. Chem., 8, 589795 (2020).

6. Grace M. Sommers, Marcos F. Calegari Andrade, Linfeng Zhang, Han Wang and Roberto Car. Raman Spectrum and Polarizability of Liquid Water from Deep Neural Networks. Phys. Chem. Chem. Phys., 22, 10592-10602 (2020).

7. Yuzhi Zhang, Chang Gao, Qianrui Liu, Linfeng Zhang, Han Wang, Mohan Chen. Warm dense matter simulation via electron temperature dependent deep potential molecular dynamics. Physics of Plasmas, 27, 122704 (2020).

8. Linfeng Zhang, Mohan Chen, Xifan Wu, Han Wang, Weinan E, and Roberto Car. Deep neural network for the dielectric response of insulators. Physical Review B, 102, 041121(R) (2020).