A universal interatomic potential for the periodic table has been developed by combining graph neural networks with three-body interactions. This M3GNet potential can perform structural relaxations, dynamic simulations and property predictions for materials across a diverse chemical space.
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References
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This is a summary of: Chen, C. et al. A universal graph deep learning interatomic potential for the periodic table. Nat. Comput. Sci. https://doi.org/10.1038/s43588-022-00349-3 (2022).
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Expanding materials science with universal many-body graph neural networks. Nat Comput Sci 2, 703–704 (2022). https://doi.org/10.1038/s43588-022-00360-8
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DOI: https://doi.org/10.1038/s43588-022-00360-8
