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

• URL: http://arxiv.org/abs/2501.19179v1
• Date: Fri, 31 Jan 2025 14:43:22 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 modeling of non-local interactions and the high computational cost of MPNNs.<n>A series of benchmarks show that CELLI can extend the strictly local Allegro architecture to model highly non-local interactions and charge transfer.<n>Our architecture generalizes to diverse datasets and large structures, achieving an accuracy comparable to MPNNs at about twice the computational efficiency.
• 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. By propagating local information to distance particles, Message-passing neural networks (MPNNs) extend the locality concept to model interactions beyond their local neighborhood. Still, this locality precludes modeling long-range effects, such as charge transfer, electrostatic interactions, and dispersion effects, which are critical to adequately describe many real-world systems. In this work, we propose the Charge Equilibration Layer for Long-range Interactions (CELLI) to address the challenging modeling of non-local interactions and the high computational cost of MPNNs. This novel architecture generalizes the fourth-generation high-dimensional neural network (4GHDNN) concept, integrating the charge equilibration (Qeq) method into a model-agnostic building block for modern equivariant GNN potentials. A series of benchmarks show that CELLI can extend the strictly local Allegro architecture to model highly non-local interactions and charge transfer. Our architecture generalizes to diverse datasets and large structures, achieving an accuracy comparable to MPNNs at about twice the computational efficiency.

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