Node-equivariant message passing for efficient and accurate machine learning interatomic potentials.

ABSTRACT: Machine learned interatomic potentials, particularly equivariant message-passing (MP) models, have demonstrated high fidelity in representing first-principles data, revolutionizing computational studies in materials science, biophysics, and catalysis. However, these equivariant MP models still incur substantial computational and memory needs due to their expensive tensor product operations over edge space, significantly limiting their applicability in large-scale or long-time simulations. In this work, we propose a novel node-equivariant MP (NEMP) framework that performs equivariant operations between the central node and a virtual summed node encoding structure information of its neighbors. Crucially, NEMP maintains comparable or even superior accuracy across diverse test systems-includin

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC12766320 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature