| Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties T Xie, JC Grossman Physical review letters 120 (14), 145301, 2018 | 2396 | 2018 |
| Crystal diffusion variational autoencoder for periodic material generation T Xie, X Fu, OE Ganea, R Barzilay, T Jaakkola arXiv preprint arXiv:2110.06197, 2021 | 270 | 2021 |
| Machine learning enabled computational screening of inorganic solid electrolytes for suppression of dendrite formation in lithium metal anodes Z Ahmad, T Xie, C Maheshwari, JC Grossman, V Viswanathan ACS central science 4 (8), 996-1006, 2018 | 264 | 2018 |
| 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 | 216 | 2022 |
| Patterning two-dimensional chalcogenide crystals of Bi2Se3 and In2Se3 and efficient photodetectors W Zheng, T Xie, Y Zhou, YL Chen, W Jiang, S Zhao, J Wu, Y Jing, Y Wu, ... Nature Communications 6 (1), 6972, 2015 | 208 | 2015 |
| Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials T Xie, A France-Lanord, Y Wang, Y Shao-Horn, JC Grossman Nature communications 10 (1), 2667, 2019 | 154 | 2019 |
| A generative model for inorganic materials design C Zeni, R Pinsler, D Zügner, A Fowler, M Horton, X Fu, Z Wang, ... Nature, 1-3, 2025 | 139* | 2025 |
| Toward designing highly conductive polymer electrolytes by machine learning assisted coarse-grained molecular dynamics Y Wang, T Xie, A France-Lanord, A Berkley, JA Johnson, Y Shao-Horn, ... chemistry of Materials 32 (10), 4144-4151, 2020 | 107 | 2020 |
| Human-and machine-centred designs of molecules and materials for sustainability and decarbonization J Peng, D Schwalbe-Koda, K Akkiraju, T Xie, L Giordano, Y Yu, CJ Eom, ... Nature Reviews Materials 7 (12), 991-1009, 2022 | 97 | 2022 |
| Charting lattice thermal conductivity for inorganic crystals and discovering rare earth chalcogenides for thermoelectrics T Zhu, R He, S Gong, T Xie, P Gorai, K Nielsch, JC Grossman Energy & Environmental Science 14 (6), 3559-3566, 2021 | 97 | 2021 |
| Hierarchical visualization of materials space with graph convolutional neural networks T Xie, JC Grossman The Journal of chemical physics 149 (17), 2018 | 84 | 2018 |
| Predicting charge density distribution of materials using a local-environment-based graph convolutional network S Gong, T Xie, T Zhu, S Wang, ER Fadel, Y Li, JC Grossman Physical Review B 100 (18), 184103, 2019 | 64 | 2019 |
| Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties T Xie, A France-Lanord, Y Wang, J Lopez, MA Stolberg, M Hill, ... Nature communications 13 (1), 3415, 2022 | 52 | 2022 |
| Effect of chemical variations in the structure of poly (ethylene oxide)-based polymers on lithium transport in concentrated electrolytes A France-Lanord, Y Wang, T Xie, JA Johnson, Y Shao-Horn, ... Chemistry of Materials 32 (1), 121-126, 2019 | 51 | 2019 |
| The impact of large language models on scientific discovery: a preliminary study using gpt-4 MR AI4Science, MA Quantum arXiv preprint arXiv:2311.07361, 2023 | 44 | 2023 |
| Examining graph neural networks for crystal structures: limitations and opportunities for capturing periodicity S Gong, K Yan, T Xie, Y Shao-Horn, R Gomez-Bombarelli, S Ji, ... Science Advances 9 (45), eadi3245, 2023 | 42 | 2023 |
| Mattersim: A deep learning atomistic model across elements, temperatures and pressures H Yang, C Hu, Y Zhou, X Liu, Y Shi, J Li, G Li, Z Chen, S Chen, C Zeni, ... arXiv preprint arXiv:2405.04967, 2024 | 40 | 2024 |
| Inverse design of next-generation superconductors using data-driven deep generative models D Wines, T Xie, K Choudhary The Journal of Physical Chemistry Letters 14 (29), 6630-6638, 2023 | 34 | 2023 |
| Calibrating DFT formation enthalpy calculations by multifidelity machine learning S Gong, S Wang, T Xie, WH Chae, R Liu, Y Shao-Horn, JC Grossman JACS Au 2 (9), 1964-1977, 2022 | 34 | 2022 |
| Simulate time-integrated coarse-grained molecular dynamics with geometric machine learning X Fu Massachusetts Institute of Technology, 2022 | 30 | 2022 |
