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Node-Equivariant Message Passing for Efficient and Accurate Machine Learning Interatomic Potentials
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DOI：10.1039/D5SC07248D.png)
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Abstract
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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—including molecules; extended systems; and universal potential benchmarks—while achieving 1-2 orders of magnitude reduction in memory and computational costs compared to edge equivariant MP models. In fact; NEMP reaches computational efficiency comparable to that of local descriptor-based models; and enabling previously inaccessible large-scale simulations.
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