| Real-time mass spectrometric characterization of the solid–electrolyte interphase of a lithium-ion battery Y Zhou, M Su, X Yu, Y Zhang, JG Wang, X Ren, R Cao, W Xu, DR Baer, ... Nature nanotechnology 15 (3), 224-230, 2020 | 318 | 2020 |
| Atomic origins of water-vapour-promoted alloy oxidation L Luo, M Su, P Yan, L Zou, DK Schreiber, DR Baer, Z Zhu, G Zhou, ... Nature materials 17 (6), 514-518, 2018 | 126 | 2018 |
| Investigation of ion–solvent interactions in nonaqueous electrolytes using in situ liquid SIMS Y Zhang, M Su, X Yu, Y Zhou, J Wang, R Cao, W Xu, C Wang, DR Baer, ... Analytical chemistry 90 (5), 3341-3348, 2018 | 50 | 2018 |
| Chemllm: A chemical large language model D Zhang, W Liu, Q Tan, J Chen, H Yan, Y Yan, J Li, W Huang, X Yue, ... arXiv preprint arXiv:2402.06852, 2024 | 11 | 2024 |
| A brief review of continuous models for ionic solutions: the Poisson–Boltzmann and related theories M Su, Y Wang Communications in Theoretical Physics 72 (6), 067601, 2020 | 10 | 2020 |
| Poisson–Boltzmann theory with non-linear ion correlations M Su, Z Xu, Y Wang Journal of Physics: Condensed Matter 31 (35), 355101, 2019 | 7 | 2019 |
| Transferable equivariant graph neural networks for the Hamiltonians of molecules and solids Y Zhong, H Yu, M Su, X Gong, H Xiang npj Computational Materials 9 (1), 182, 2023 | 5 | 2023 |
| Transferable E (3) equivariant parameterization for hamiltonian of molecules and solids Y Zhong, H Yu, M Su, X Gong, H Xiang arXiv preprint arXiv:2210.16190, 2022 | 5 | 2022 |
| Efficient determination of the Hamiltonian and electronic properties using graph neural network with complete local coordinates M Su, JH Yang, HJ Xiang, XG Gong Machine Learning: Science and Technology 4 (3), 035010, 2023 | 4 | 2023 |
| Exploring large-lattice-mismatched interfaces with neural network potentials: the case of the CdS/CdTe heterostructure M Su, JH Yang, ZP Liu, XG Gong The Journal of Physical Chemistry C 126 (31), 13366-13372, 2022 | 4 | 2022 |
| Geometry-enhanced pretraining on interatomic potentials T Cui, C Tang, M Su, S Zhang, Y Li, L Bai, Y Dong, X Gong, W Ouyang Nature Machine Intelligence 6 (4), 428-436, 2024 | 3 | 2024 |
| Gpip: Geometry-enhanced pre-training on interatomic potentials T Cui, C Tang, M Su, S Zhang, Y Li, L Bai, Y Dong, X Gong, W Ouyang arXiv preprint arXiv:2309.15718, 2023 | 2 | 2023 |
| Efficient prediction of density functional theory hamiltonian with graph neural network M Su, JH Yang, HJ Xiang, XG Gong Preprint at https://arxiv. org/abs/2205.05475, 2022 | 2 | 2022 |
| Physical formula enhanced multi-task learning for pharmacokinetics prediction R Li, D Zhou, A Shen, A Zhang, M Su, M Li, H Chen, G Chen, Y Zhang, ... arXiv preprint arXiv:2404.10354, 2024 | 1 | 2024 |
| Online Test-time Adaptation for Interatomic Potentials S Zhang, T Cui, C Tang, D Zhou, Y Li, X Gong, W Ouyang, M Su | | 2024 |
| Online Test-time Adaptation for Interatomic Potentials T Cui, C Tang, D Zhou, Y Li, X Gong, W Ouyang, M Su, S Zhang arXiv preprint arXiv:2405.08308, 2024 | | 2024 |
| Efficient determination of the Hamiltonian and electronic properties using graph neural network X Gong, M Su, J Yang, H Xiang | | 2022 |
Zhijie XuPacific Northwest National Lab在 pnnl.gov 的电子邮件经过验证
