| Illuminating protein space with a programmable generative model JB Ingraham, M Baranov, Z Costello, KW Barber, W Wang, A Ismail, ... Nature 623 (7989), 1070-1078, 2023 | 197 | 2023 |
| Coarse-Graining Auto-Encoders for Molecular Dynamics W Wang, R Gómez-Bombarelli npj Computational Materials, 2019, 2019 | 174 | 2019 |
| Forces are not enough: Benchmark and critical evaluation for machine learning force fields with molecular simulations X Fu, Z Wu, W Wang, T Xie, S Keten, R Gomez-Bombarelli, T Jaakkola arXiv preprint arXiv:2210.07237, 2022 | 127 | 2022 |
| An end-to-end framework for molecular conformation generation via bilevel programming M Xu, W Wang, S Luo, C Shi, Y Bengio, R Gomez-Bombarelli, J Tang International conference on machine learning, 11537-11547, 2021 | 80 | 2021 |
| Active learning accelerates ab initio molecular dynamics on reactive energy surfaces SJ Ang, W Wang, D Schwalbe-Koda, S Axelrod, R Gómez-Bombarelli Chem, 2021 | 60 | 2021 |
| Active Learning and Neural Network Potentials Accelerate Molecular Screening of Ether-based Solvate Ionic Liquids W Wang, T Yang, WH Harris, R Gómez-Bombarelli Chemical Communications, 2020, 2020 | 50 | 2020 |
| Temperature-transferable coarse-graining of ionic liquids with dual graph convolutional neural networks J Ruza, W Wang, D Schwalbe-Koda, S Axelrod, WH Harris, ... The Journal of chemical physics 153 (16), 2020 | 46 | 2020 |
| Differentiable molecular simulations for control and learning W Wang, S Axelrod, R Gómez-Bombarelli arXiv preprint arXiv:2003.00868, 2020 | 41 | 2020 |
| Generative coarse-graining of molecular conformations W Wang, M Xu, C Cai, BK Miller, T Smidt, Y Wang, J Tang, ... International Conference on Machine Learning (ICML), 2022, 2022 | 32 | 2022 |
| Holliday junction thermodynamics and structure: Coarse-grained simulations and experiments W Wang, LM Nocka, BZ Wiemann, DM Hinckley, I Mukerji, FW Starr Scientific reports 6 (1), 22863, 2016 | 31 | 2016 |
| Learning pair potentials using differentiable simulations W Wang, Z Wu, JCB Dietschreit, R Gómez-Bombarelli The Journal of Chemical Physics 158 (4), 2023 | 17 | 2023 |
| Active learning accelerates ab initio molecular dynamics on pericyclic reactive energy surfaces SJ Ang, W Wang, D Schwalbe-Koda, S Axelrod, R Gomez-Bombarelli ChemRxiv, 2020 | 1 | 2020 |
| Learning Coarse-Grained Particle Latent Space with Auto-Encoders W Wang, R Gómez-Bombarelli Second Workshop on Machine Learning and the Physical Sciences (NeurIPS 2019 …, 2019 | 1 | 2019 |
| Holliday junction thermodynamics and structure: comparisons of coarse-grained simulations and experiments FW Starr, W Wang, LM Nocka, BZ Wiemann, DM Hinckley, I Mukerji Biophysical Journal 110 (3), 178a, 2016 | 1 | 2016 |
| Differentiable Multiscale Molecular Simulations W Wang Massachusetts Institute of Technology, 2022 | | 2022 |
| Differentiable Molecular Simulations W Wang, S Axelrod, R Gomez-Bombarelli APS March Meeting Abstracts 2021, B22. 012, 2021 | | 2021 |
| Investigation of the Melting Thermodynamics of a DNA 4-Way Junction: One Base at a Time RE Savage, W Wang, FW Starr, I Mukerji Biophysical Journal 112 (3), 69a-70a, 2017 | | 2017 |
| Differentiable Molecular Simulations for Learning and Control W Wang, S Axelrod, R Gómez-Bombarelli | | |
| This journal is© The Royal Society of Chemistry 2020 B Wang, C Zhao | | |
Simon AxelrodD. E. Shaw Research在 deshawresearch.com 的电子邮件经过验证
