| Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential S Zhang, MZ Makoś, RB Jadrich, E Kraka, K Barros, BT Nebgen, S Tretiak, ... Nature Chemistry 16 (5), 727-734, 2024 | 56 | 2024 |
| Bioinspired interfacial chelating-like reinforcement strategy toward mechanically enhanced lamellar materials K Chen, S Zhang, A Li, X Tang, L Li, L Guo ACS nano 12 (5), 4269-4279, 2018 | 46 | 2018 |
| A general bioinspired, metals-based synergic cross-linking strategy toward mechanically enhanced materials K Chen, J Ding, S Zhang, X Tang, Y Yue, L Guo ACS nano 11 (3), 2835-2845, 2017 | 42 | 2017 |
| Machine learning of reactive potentials Y Yang, S Zhang, KD Ranasinghe, O Isayev, AE Roitberg Annual Review of Physical Chemistry 75 (1), 371-395, 2024 | 20 | 2024 |
| Large-scale self-assembly of uniform submicron silver sulfide material driven by precise pressure control J Qi, K Chen, S Zhang, Y Yang, L Guo, S Yang Nanotechnology 28 (10), 105606, 2017 | 1 | 2017 |
| Including physics-informed atomization constraints in neural networks for reactive chemistry S Zhang, M Chigaev, O Isayev, R Messerly, N Lubbers | | 2024 |
| BalanceNet: An uncertainty-based mixture-of-experts model architecture for machine learning potential OI Shuhao Zhang, Amogh Tundlam https://doi.org/10.1021/scimeetings.4c10344, 2024 | | 2024 |
| Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential R Messerly, S Zhang, M Makoś, R Jadrich, E Kraka, K Barros, B Nebgen, ... | | 2023 |
| Nanoreactor active learning: Discovering chemistry with a general reactive machine learning potential R Messerly, J Smith, S Zhang, N Lubbers, O Isayev, S Tretiak, B Nebgen, ... APS March Meeting Abstracts 2023, RR06. 006, 2023 | | 2023 |