| 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 | 1902 | 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 | 1280 | 2017 |
| 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 | 535 | 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 | 529 | 2020 |
| 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 | 519 | 2018 |
| 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 | 319 | 2009 |
| Phase diagram of a deep potential water model L Zhang, H Wang, R Car, W E Physical review letters 126 (23), 236001, 2021 | 317 | 2021 |
| 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 | 290 | 2020 |
| 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 | 207 | 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 | 180 | 2021 |
| 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 | 176 | 2023 |
| 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 | 144 | 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 | 143 | 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 | 128 | 2022 |
| 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 | 121 | 2013 |
| Deep potentials for materials science T Wen, L Zhang, H Wang, E Weinan, DJ Srolovitz Materials Futures 1 (2), 022601, 2022 | 117 | 2022 |
| Deep neural network for the dielectric response of insulators L Zhang, M Chen, X Wu, H Wang, W E, R Car Physical Review B 102 (4), 041121, 2020 | 115 | 2020 |
| 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 | 92 | 2021 |
| Reinforced dynamics for enhanced sampling in large atomic and molecular systems L Zhang, H Wang, W E The Journal of Chemical Physics, 2018 | 84 | 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 | 78 | 2014 |
Luigi Delle SiteInstitute for Mathematics, Department of Mathematics and Computer Science, Freie Universität BerlinBestätigte E-Mail-Adresse bei fu-berlin.de