EAC-Net: Predicting real-space charge density via equivariant atomic contributions

Abstract

Charge density is central to density functional theory (DFT), as it fully defines the ground-state properties of a material system. Obtaining it with high accuracy is a computational bottleneck. Existing machine learning models are constrained by trade-offs among accuracy, efficiency, and generalization. Here, we introduce the Equivariant Atomic Contribution Network (EAC-Net), which couples atoms and grids to integrate the strengths of grid-based and basis-function frameworks. EAC-Net achieves high accuracy (typically below 1% error), enhanced efficiency, and strong generalization across complex systems. Building on this framework, we develop EAC-mp, a universal charge density model covering the periodic table. The model demonstrates robust zero-shot performance across diverse systems, and generalizes beyond the training distribution, supporting downstream applications such as band structure calculations. By linking local chemical environments to charge densities, EAC-Net provides a scalable framework for accelerating electronic structure prediction and enabling high-throughput materials discovery.