| Anomalous phase separation in a correlated electron system: Machine-learning–enabled large-scale kinetic Monte Carlo simulations S Zhang, P Zhang, GW Chern Proceedings of the National Academy of Sciences 119 (18), e2119957119, 2022 | 14 | 2022 |
| Convolutional neural networks for large-scale dynamical modeling of itinerant magnets X Cheng, S Zhang, PCH Nguyen, S Azarfar, GW Chern, SS Baek Physical Review Research 5 (3), 033188, 2023 | 10 | 2023 |
| Machine learning predictions for local electronic properties of disordered correlated electron systems YH Liu, S Zhang, P Zhang, TK Lee, GW Chern Physical Review B 106 (3), 035131, 2022 | 9 | 2022 |
| Machine learning for phase ordering dynamics of charge density waves C Cheng, S Zhang, GW Chern Physical Review B 108 (1), 014301, 2023 | 8 | 2023 |
| Descriptors for machine learning model of generalized force field in condensed matter systems P Zhang, S Zhang, GW Chern arXiv preprint arXiv:2201.00798, 2022 | 5 | 2022 |
| Machine learning for structure-property mapping of Ising models: Scalability and limitations Z Tian, S Zhang, GW Chern Physical Review E 108 (6), 065304, 2023 | 4 | 2023 |
| Nonequilibrium dynamics of gating-induced resistance transition in charge density wave Insulators S Zhang, GW Chern arXiv preprint arXiv:2201.02194, 2022 | 2 | 2022 |
| Machine learning approach for vibronically renormalized electronic band structures N Aryal, S Zhang, W Yin, GW Chern arXiv preprint arXiv:2409.01523, 2024 | | 2024 |
| Kinetics of orbital ordering in cooperative Jahn-Teller models: Machine-learning enabled large-scale simulations S Ghosh, S Zhang, C Cheng, GW Chern arXiv preprint arXiv:2405.14776, 2024 | | 2024 |
| Coarsening of chiral domains in itinerant electron magnets: A machine learning force field approach Y Fan, S Zhang, GW Chern arXiv preprint arXiv:2403.11705, 2024 | | 2024 |
| Machine learning force-field models for metallic spin glass M Shi, S Zhang, GW Chern arXiv preprint arXiv:2311.16964, 2023 | | 2023 |
