Linfeng Zhang
Linfeng Zhang
DP Technology; AI for Science Institute
Verified email at
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
Physical review letters 120 (14), 143001, 2018
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
H Wang, L Zhang, J Han, W E
Computer Physics Communications 228, 178-184, 2018
Active learning of uniformly accurate interatomic potentials for materials simulation
L Zhang, DY Lin, H Wang, R Car, W E
Physical Review Materials 3 (2), 023804, 2019
End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems
L Zhang, J Han, H Wang, W Saidi, R Car, E Weinan
Advances in Neural Information Processing Systems, 4441-4451, 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
Scientific discovery in the age of artificial intelligence
H Wang, T Fu, Y Du, W Gao, K Huang, Z Liu, P Chandak, S Liu, ...
Nature 620 (7972), 47-60, 2023
Deep potential: A general representation of a many-body potential energy surface
J Han, L Zhang, R Car, W E
Communications in Computational Physics 23 (3), 629-639, 2018
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, W E
Physical review letters 126 (23), 236001, 2021
Solving many-electron Schrödinger equation using deep neural networks
J Han, L Zhang, E Weinan
Journal of Computational Physics, 108929, 2019
The limits of the nuclear landscape explored by the relativistic continuum Hartree–Bogoliubov theory
XW Xia, Y Lim, PW Zhao, HZ Liang, XY Qu, Y Chen, H Liu, LF Zhang, ...
Atomic Data and Nuclear Data Tables 121, 1-215, 2018
DeePCG: Constructing coarse-grained models via deep neural networks
L Zhang, J Han, H Wang, R Car, W E
The Journal of chemical physics 149 (3), 034101, 2018
Free energy of proton transfer at the water–TiO 2 interface from ab initio deep potential molecular dynamics
MFC Andrade, HY Ko, L Zhang, R Car, A Selloni
Chemical Science 11 (9), 2335-2341, 2020
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
Signatures of a liquid–liquid transition in an ab initio deep neural network model for water
TE Gartner III, L Zhang, PM Piaggi, R Car, AZ Panagiotopoulos, ...
Proceedings of the National Academy of Sciences 117 (42), 26040-26046, 2020
Uni-mol: A universal 3d molecular representation learning framework
G Zhou, Z Gao, Q Ding, H Zheng, H Xu, Z Wei, L Zhang, G Ke
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
When do short-range atomistic machine-learning models fall short?
S Yue, MC Muniz, MF Calegari Andrade, L Zhang, R Car, ...
The Journal of Chemical Physics 154 (3), 2021
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
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
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