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Description
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All-atom dynamics simulations are an indispensable quantitative tool in physics, chemistry, and materials science, but large systems and long simulation times remain challenging due to the trade-off between computational efficiency and predictive accuracy. To address this challenge, we combine effective two- and three-body potentials in a cubic B-spline basis with regularized linear regression to obtain machine-learning potentials that are physically interpretable, sufficiently accurate for applications, as fast as the fastest traditional empirical potentials, and two to four orders of magnitude faster than state-of-the-art machine-learning potentials. For data from empirical potentials, we demonstrate the exact retrieval of the potential. For data from density functional theory, the predicted energies, forces, and derived properties, including phonon spectra, elastic constants, and melting points, closely match those of the reference method. The introduced potentials might contribute towards accurate all-atom dynamics simulations of large atomistic systems over long-time scales. (2023-09-02)
***This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.1038/s41524-023-01092-7 for the original and latest version of the publication*** (2026-07-01)
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Keyword
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Computer science, Eigenvalues and eigenvectors, Statistical physics, Phonon, Atom (system on chip), Basis (linear algebra), Molecular dynamics, Artificial intelligence, Algorithm, Machine learning, Physics, Mathematics, Quantum mechanics |
