A Fast, Accurate, and Reactive Equivariant Foundation Potential

TL;DR

This paper introduces QET, a charge-aware, equivariant potential that scales linearly with system size, accurately modeling electrostatics and charge transfer in materials, enabling advanced simulations in energy and catalysis.

Contribution

The paper presents QET, a novel charge-equilibrated TensorNet architecture that achieves linear scaling and improves modeling of charge transfer in materials.

Findings

QET matches state-of-the-art potentials on benchmarks.

QET accurately predicts ionic liquid structures.

QET captures reactive processes at interfaces.

Abstract

Electrostatics govern charge transfer and reactivity in materials. Yet, most foundation potentials (FPs) either do not explicitly model such interactions or pay a prohibitive scaling penalty to do so. Here, we introduce charge-equilibrated TensorNet (QET), an equivariant, charge-aware architecture that attains 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 standard materials property benchmarks but delivers qualitatively different predictions in systems dominated by charge transfer. The QET FP reproduces the correct structure and density of the NaCl-CaCl2 ionic liquid, 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 the atomistic simulation of accurate electrostatics at scale and establish a general, data-driven framework for charge-aware FPs with transformative applications in energy storage, catalysis, and beyond.