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Machine learning interatomic potentials at the centennial crossroads of quantum mechanics

Nature Computational Science volume 5, pages 1120–1132 (2025)Cite this article

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Abstract

As quantum mechanics marks its centennial in 2025, machine learning interatomic potentials have emerged as transformative tools in molecular modeling, bridging quantum mechanical accuracy with classical efficiency. Here we examine their development through four defining challenges—achieving chemical accuracy, maintaining computational efficiency, ensuring interpretability and reaching universal generalizability. We highlight architectural innovations, physics-informed approaches, and foundation models trained on extensive data. Together, these developments chart a path toward predictive, transferable and physically grounded machine learning frameworks for next-generation computational chemistry.

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Fig. 1: Timeline of a hundred years of QM evolution along with concurrent AI events, with key milestones for AI in quantum chemistry.
Fig. 2: Evolution of MLIP architectures.
Fig. 3: Comparative accuracy and scaling behavior of MLIPs.
Fig. 4: Performance of MLIPs for open-shell bond-dissociation.

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Acknowledgements

We acknowledge support by the National Science Foundation (NSF) through the Center for Computer-Assisted Synthesis (C-CAS) CHE-2202693 award and the Office of Naval Research (ONR) through the Energetic Materials Program (MURI grant no. N00014-21-1-2476). This research is part of the Frontera computing project at the Texas Advanced Computing Center. Frontera is made possible by NSF award OAC-1818253.

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  1. Department of Chemistry, Mellon College of Science, Carnegie Mellon University, Pittsburgh, PA, USA

    Bhupalee Kalita, Hatice Gokcan & Olexandr Isayev

  2. Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    Olexandr Isayev

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Kalita, B., Gokcan, H. & Isayev, O. Machine learning interatomic potentials at the centennial crossroads of quantum mechanics. Nat Comput Sci 5, 1120–1132 (2025). https://doi.org/10.1038/s43588-025-00930-6

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