Han Wang (王涵)
Han Wang (王涵)
Institute of Applied Physics and Computational Mathematics
Verified email at - Homepage
Cited by
Cited by
Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
L Zhang, J Han, H Wang, R Car, W E
arXiv preprint arXiv:1707.09571, 2017
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
H Wang, L Zhang, J Han, W E
arXiv preprint arXiv:1712.03641, 2017
Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation
L Zhang, DY Lin, H Wang, R Car, W E
arXiv preprint arXiv:1810.11890, 2018
End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems
L Zhang, J Han, H Wang, WA Saidi, R Car, W E
arXiv preprint arXiv:1805.09003, 2018
DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models
Y Zhang, H Wang, W Chen, J Zeng, L Zhang, H Wang, E Weinan
Computer Physics Communications 253, 107206, 2020
Comparative atomistic and coarse-grained study of water: What do we lose by coarse-graining?
H Wang, C Junghans, K Kremer
The European Physical Journal E: Soft Matter and Biological Physics 28 (2 …, 2009
Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning
W Jia, H Wang, M Chen, D Lu, L Lin, R Car, E Weinan, L Zhang
SC20: International Conference for High Performance Computing, Networking …, 2020
Phase Diagram of a Deep Potential Water Model
L Zhang, H Wang, R Car, E Weinan
Physical Review Letters 126 (23), 236001, 2021
DeePCG: constructing coarse-grained models via deep neural networks
L Zhang, J Han, H Wang, R Car, W E
arXiv preprint arXiv:1802.08549, 2018
86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy
D Lu, H Wang, M Chen, L Lin, R Car, E Weinan, W Jia, L Zhang
Computer Physics Communications 259, 107624, 2021
Optimizing working parameters of the smooth particle mesh Ewald algorithm in terms of accuracy and efficiency
H Wang, F Dommert, C Holm
The Journal of chemical physics 133, 034117, 2010
Raman spectrum and polarizability of liquid water from deep neural networks
GM Sommers, MFC Andrade, L Zhang, H Wang, R Car
Physical Chemistry Chemical Physics 22 (19), 10592-10602, 2020
Grand-canonical-like molecular-dynamics simulations by using an adaptive-resolution technique
H Wang, C Hartmann, C Schütte, L Delle Site
Physical Review X 3 (1), 011018, 2013
Deep neural network for the dielectric response of insulators
L Zhang, M Chen, X Wu, H Wang, E Weinan, R Car
Physical Review B 102 (4), 041121, 2020
A deep potential model with long-range electrostatic interactions
L Zhang, H Wang, MC Muniz, AZ Panagiotopoulos, R Car, W E
The Journal of Chemical Physics 156 (12), 124107, 2022
Deep potentials for materials science
T Wen, L Zhang, H Wang, E Weinan, DJ Srolovitz
Materials Futures 1 (2), 022601, 2022
DeePMD-kit v2: A software package for deep potential models
J Zeng, D Zhang, D Lu, P Mo, Z Li, Y Chen, M Rynik, L Huang, Z Li, S Shi, ...
The Journal of Chemical Physics 159 (5), 2023
Reinforced dynamics for enhanced sampling in large atomic and molecular systems
L Zhang, H Wang, W E
The Journal of Chemical Physics, 2018
Applications of the cross-entropy method to importance sampling and optimal control of diffusions
W Zhang, H Wang, C Hartmann, M Weber, C Schütte
SIAM Journal on Scientific Computing 36 (6), A2654-A2672, 2014
Deep potential generation scheme and simulation protocol for the Li10GeP2S12-type superionic conductors
J Huang, L Zhang, H Wang, J Zhao, J Cheng, W E
The Journal of Chemical Physics 154 (9), 094703, 2021
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