Feb. 13, 2024, 5:44 a.m. | Bingqing Cheng

cs.LG updates on arXiv.org arxiv.org

Machine learning interatomic potentials are revolutionizing large-scale, accurate atomistic modelling in material science and chemistry. These potentials often use atomic cluster expansion or equivariant message passing with spherical harmonics as basis functions. However, the dependence on Clebsch-Gordan coefficients for maintaining rotational symmetry leads to computational inefficiencies and redundancies. We propose an alternative: a Cartesian-coordinates-based atomic density expansion. This approach provides a complete description of atomic environments while maintaining interaction body orders. Additionally, we integrate low-dimensional embeddings of various chemical elements …

chemistry cluster computational cs.lg expansion functions leads machine machine learning material modelling physics.chem-ph physics.comp-ph scale science symmetry

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