| 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
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A close look at Google’s GNoME inorganic materials dataset (Merchant et al 2023 Nature 624 80–85), and 11 things you would like to know.
| [1] |
Merchant A, Batzner S, Schoenholz S S, Aykol M, Cheon G and Cubuk E D 2023 Scaling deep learning for materials discovery Nature 624 80–85
|
| [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
|
| [5] |
Saal J E, Kirklin S, Aykol M, Meredig B and Wolverton C 2013 Materials design and discovery with highthroughput density functional theory: the open quantum materials database (OQMD) JOM 65 1501–9
|
| [6] |
Chen C and Ong S P 2022 A universal graph deep learning interatomic potential for the periodic table Nat. Comput. Sci. 2 718–28
|
| [7] |
Deng B, Zhong P, Jun K, Riebesell J, Han K, Bartel C J and Ceder G 2023 CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling Nat. Mach. Intell. 5 1031–41
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