| Citation: | Miao Liu, Sheng Meng. Recent breakthrough in AI-driven materials science: tech giants introduce groundbreaking models[J]. Materials Futures, 2024, 3(2): 027501. doi: 10.1088/2752-5724/ad2e0c
|
| [1] |
Merchant A, Batzner S, Schoenholz S S, Aykol M, Cheon G, Cubuk E D 2023 Scaling deep learning for materials discovery Nature 624 80-85 doi: 10.1038/s41586-023-06735-9
|
| [2] |
Zeni C, et al 2023 MatterGen: a generative model for inorganic materials design (arXiv:2312.03687)
|
| [3] |
Chen C, et al 2024 Accelerating computational materials discovery with artificial intelligence and cloud high-performance computing: from large-scale screening to experimental validation (arXiv:2401.04070)
|
| [4] |
Jain A, et al 2013 Commentary: the materials project: a materials genome approach to accelerating materials innovation APL Mater. 1 011002 doi: 10.1063/1.4812323
|
| [5] |
Saal J E, Kirklin S, Aykol M, Meredig B, Wolverton C 2013 Materials design and discovery with high-throughput density functional theory: the open quantum materials database (OQMD) JOM 65 1501-9 doi: 10.1007/s11837-013-0755-4
|
| [6] |
Chen C, Ong S P 2022 A universal graph deep learning interatomic potential for the periodic table Nat. Comput. Sci. 2 718-28 doi: 10.1038/s43588-022-00349-3
|
| [7] |
Deng B, Zhong P, Jun K, Riebesell J, Han K, Bartel C J, Ceder G 2023 CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling Nat. Mach. Intell. 5 1031-41 doi: 10.1038/s42256-023-00716-3
|
Figures(4)
Copyright © 2021 Materials Futures Editorial Office
Supported by:
Beijing Renhe Information Technology Co., Ltd.
DownLoad:
