A Linear-Scaling, Charge-Aware Foundation Potential for Atomistic Simulations

Abstract

Electrostatics govern charge transfer and reactivity in materials. However, most foundation potentials (FPs) either neglect explicit electrostatic interactions or come at prohibitive computational cost. Here, we introduce charge-equilibrated TensorNet (QET), an equivariant, charge-aware architecture that achieves linear scaling with system size via an analytically solvable charge-equilibration scheme. We demonstrate that a trained QET FP matches state-of-the-art FPs on materials property benchmarks but delivers qualitatively different predictions in systems dominated by electrostatic interactions. The QET FP reproduces the correct structure and density of the NaCl-CaCl2 ionic liquid and the crystallization of Ge1Sb2Te4 phase change memory, which charge-agnostic FPs miss. We further show that a fine-tuned QET captures reactive processes at the Li/Li6PS5Cl solid-electrolyte interface and supports simulations under applied electrochemical potentials. These results remove a fundamental constraint in large-scale atomistic simulations of electrostatics and establish a general, data-driven framework for charge-aware FPs with transformative applications in energy storage, catalysis, and beyond.