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

원문 초록

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.