| Solving high-dimensional partial differential equations using deep learning J Han, A Jentzen, W E Proceedings of the National Academy of Sciences 115 (34), 8505-8510, 2018 | 2094 | 2018 |
| 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 | 2018 | 2018 |
| DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics H Wang, L Zhang, J Han, E Weinan Computer Physics Communications 228, 178-184, 2018 | 1415 | 2018 |
| String method for the study of rare events E Weinan, W Ren, E Vanden-Eijnden Physical Review B 66 (5), 052301, 2002 | 1389* | 2002 |
| A proposal on machine learning via dynamical systems E Weinan Communications in Mathematics and Statistics 5 (1), 1-11, 2017 | 699 | 2017 |
| Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations J Han, A Jentzen Communications in mathematics and statistics 5 (4), 349-380, 2017 | 694 | 2017 |
| Onsager's conjecture on the energy conservation for solutions of Euler's equation P Constantin, W E, ES Titi | 660 | 1994 |
| 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 | 596 | 2020 |
| 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 | 555 | 2019 |
| The heterognous multiscale methods E Weinan, B Engquist Communications in Mathematical Sciences 1 (1), 87-132, 2003 | 545 | 2003 |
| Principles of multiscale modeling E Weinan Cambridge University Press, 2011 | 535 | 2011 |
| Heterogeneous multiscale methods: a review E Weinan, B Engquist, X Li, W Ren, E Vanden-Eijnden Communications in computational physics 2 (3), 367-450, 2007 | 510 | 2007 |
| Transition-path theory and path-finding algorithms for the study of rare events. E Vanden-Eijnden Annual review of physical chemistry 61, 391-420, 2010 | 501 | 2010 |
| The heterogeneous multiscale method A Abdulle, E Weinan, B Engquist, E Vanden-Eijnden Acta Numerica 21, 1-87, 2012 | 495 | 2012 |
| Finite temperature string method for the study of rare events E Weinan, W Ren, E Vanden-Eijnden j. Phys. Chem. B 109 (14), 6688-6693, 2005 | 433 | 2005 |
| Towards a theory of transition paths E Vanden-Eijnden Journal of statistical physics 123 (3), 503-523, 2006 | 378 | 2006 |
| Phase diagram of a deep potential water model L Zhang, H Wang, R Car, W E Physical review letters 126 (23), 236001, 2021 | 352 | 2021 |
| Stochastic modified equations and adaptive stochastic gradient algorithms Q Li, C Tai, E Weinan International Conference on Machine Learning, 2101-2110, 2017 | 338 | 2017 |
| Heterogeneous multiscale method: a general methodology for multiscale modeling E Weinan, B Engquist, Z Huang Physical Review B 67 (9), 092101, 2003 | 317 | 2003 |
| 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 | 312 | 2020 |
