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Albert Musaelian, Simon Batzner, Anders Johansson, Lixin Sun, Cameron J. Owen, Mordechai Kornbluth, Boris Kozinsky
A simultaneously accurate and computationally efficient parametrization of the potential energy surface of molecules and materials is a long-standing goal in the natural sciences. While atom-centered message passing neural networks (MPNNs) have shown remarkable accuracy, their information propagation has limited the accessible length-scales. Local methods, conversely, scale to large simulations but have suffered from inferior accuracy. This work introduces Allegro, a strictly local equivariant deep neural network interatomic potential architecture that simultaneously exhibits excellent accuracy and scalability. Allegro represents a many-body potential using iterated tensor products of learned equivariant representations without atom-centered message passing. Allegro obtains improvements over state-of-the-art methods on QM9 and revMD17. A single tensor product layer outperforms existing deep MPNNs and transformers on QM9. Furthermore, Allegro displays remarkable generalization to out-of-distribution data. Molecular simulations using Allegro recover structural and kinetic properties of an amorphous electrolyte in excellent agreement with ab-initio simulations. Finally, we demonstrate parallelization with a simulation of 100 million atoms.
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Musaelian, A., Batzner, S., Johansson, A., Sun, L., Owen, C. J., Kornbluth, M., & Kozinsky, B. (2023). Learning local equivariant representations for large-scale atomistic dynamics. Nature Communications, 14(1), 579. https://doi.org/10.1038/s41467-023-36329-y
Musaelian, Albert, et al. "Learning local equivariant representations for large-scale atomistic dynamics." Nature Communications, vol. 14, no. 1, 2023, pp. 579, https://doi.org/10.1038/s41467-023-36329-y.
Musaelian, Albert, Simon Batzner, Anders Johansson, Lixin Sun, Cameron J. Owen, Mordechai Kornbluth, and Boris Kozinsky. "Learning local equivariant representations for large-scale atomistic dynamics." Nature Communications 14, no. 1 (2023): 579. https://doi.org/10.1038/s41467-023-36329-y.
Musaelian A., Batzner S., Johansson A. [и др.] Learning local equivariant representations for large-scale atomistic dynamics // Nature Communications. — 2023. — Vol. 14, № 1. — P. 579. — URL: https://doi.org/10.1038/s41467-023-36329-y
@article{musaelian2023learning,
title = {{Learning local equivariant representations for large-scale atomistic dynamics}},
author = {Albert Musaelian and Simon Batzner and Anders Johansson and Lixin Sun and Cameron J. Owen and Mordechai Kornbluth and Boris Kozinsky},
journal = {Nature Communications},
year = {2023},
volume = {14},
number = {1},
pages = {579},
doi = {10.1038/s41467-023-36329-y},
url = {https://doi.org/10.1038/s41467-023-36329-y}
}TY - JOUR AU - Musaelian, Albert AU - Batzner, Simon AU - Johansson, Anders AU - Sun, Lixin AU - Owen, Cameron J. AU - Kornbluth, Mordechai AU - Kozinsky, Boris TI - Learning local equivariant representations for large-scale atomistic dynamics JO - Nature Communications VL - 14 IS - 1 SP - 579 PY - 2023 DA - 2023/02/03 DO - 10.1038/s41467-023-36329-y UR - https://doi.org/10.1038/s41467-023-36329-y ER -
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