Learning Non-Local Molecular Interactions via Equivariant Local Representations and Charge Equilibration

• URL: http://arxiv.org/abs/2501.19179v2
• Date: Fri, 27 Jun 2025 16:03:53 GMT
• Title: Learning Non-Local Molecular Interactions via Equivariant Local Representations and Charge Equilibration
• Authors: Paul Fuchs, Michał Sanocki, Julija Zavadlav,
• Abstract summary: We propose the Charge Equilibration Layer for Long-range Interactions (CELLI) to address the challenge of efficiently modeling non-local interactions.<n>CELLI generalizes to diverse datasets and large structures while providing high computational efficiency and robust predictions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph Neural Network (GNN) potentials relying on chemical locality offer near-quantum mechanical accuracy at significantly reduced computational costs. Message-passing GNNs model interactions beyond their immediate neighborhood by propagating local information between neighboring particles while remaining effectively local. However, locality precludes modeling long-range effects critical to many real-world systems, such as charge transfer, electrostatic interactions, and dispersion effects. In this work, we propose the Charge Equilibration Layer for Long-range Interactions (CELLI) to address the challenge of efficiently modeling non-local interactions. This novel architecture generalizes the classical charge equilibration (Qeq) method to a model-agnostic building block for modern equivariant GNN potentials. Therefore, CELLI extends the capability of GNNs to model long-range interactions while providing high interpretability through explicitly modeled charges. On benchmark systems, CELLI achieves state-of-the-art results for strictly local models. CELLI generalizes to diverse datasets and large structures while providing high computational efficiency and robust predictions.

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