Deep learning has the potential to accelerate atomistic simulations, but existing models suffer from a lack of robustness, sample efficiency, and accuracy. Simon Batzner, Albert Musaelian, and Boris Kozinsky outline how exploiting the symmetry of Euclidean space offers a new way to address these challenges.
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Acknowledgements
The authors are indebted to many contributions from A. Johansson, L. Sun, M. Geiger, and T. Smidt in developing the ideas and their software implementation.
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Batzner, S., Musaelian, A. & Kozinsky, B. Advancing molecular simulation with equivariant interatomic potentials. Nat Rev Phys 5, 437–438 (2023). https://doi.org/10.1038/s42254-023-00615-x
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DOI: https://doi.org/10.1038/s42254-023-00615-x
