| Lu–H–N phase diagram from first-principles calculations F Xie, T Lu, Z Yu, Y Wang, Z Wang, S Meng, M Liu Chinese Physics Letters 40 (5), 057401, 2023 | 44 | 2023 |
| A universal model for accurately predicting the formation energy of inorganic compounds Y Liang, M Chen, Y Wang, H Jia, T Lu, F Xie, G Cai, Z Wang, S Meng, ... Science China Materials 66 (1), 343-351, 2023 | 17 | 2023 |
| Direct measurement of adhesions of liquids on graphite C Qu, W Cao, B Liu, A Wang, F Xie, M Ma, W Shan, M Urbakh, Q Zheng The Journal of Physical Chemistry C 123 (18), 11671-11676, 2019 | 13 | 2019 |
| MatChat: A large language model and application service platform for materials science ZY Chen, FK Xie, M Wan, Y Yuan, M Liu, ZG Wang, S Meng, YG Wang Chinese Physics B 32 (11), 118104, 2023 | 12 | 2023 |
| Predicting structure-dependent Hubbard U parameters via machine learning G Cai, Z Cao, F Xie, H Jia, W Liu, Y Wang, F Liu, X Ren, S Meng, M Liu Materials Futures 3 (2), 025601, 2024 | 3 | 2024 |
| A universal model for the formation energy prediction of inorganic compounds Y Liang, M Chen, Y Wang, H Jia, T Lu, F Xie, S Meng, M Liu arXiv preprint arXiv:2108.00349, 2021 | 2 | 2021 |
| GPTFF: A high-accuracy out-of-the-box universal AI force field for arbitrary inorganic materials F Xie, T Lu, S Meng, M Liu arXiv preprint arXiv:2402.19327, 2024 | 1 | 2024 |
| Predicting structure-dependent Hubbard U parameters for assessing hybrid functional-level exchange via machine learning Z Cao, G Cai, F Xie, H Jia, W Liu, Y Wang, F Liu, X Ren, S Meng, M Liu arXiv preprint arXiv:2302.09507, 2023 | | 2023 |
