V2Rho-FNO: Fourier Neural Operator for Electronic Density Prediction

TL;DR

This paper introduces V2Rho-FNO, a Fourier Neural Operator framework that predicts electron densities from external potentials, enabling fast, transferable, and accurate electronic structure predictions across diverse molecules without retraining.

Contribution

The paper presents a novel neural operator approach that directly learns the potential-to-density mapping, surpassing traditional methods by capturing global interactions and enabling zero-shot generalization.

Findings

Achieves accurate electron density predictions across multiple elements and geometries.

Demonstrates zero-shot transferability to unseen molecular systems.

Outperforms conventional methods in speed and generalization capability.

Abstract

Density functional theory (DFT) is a cornerstone of computational chemistry and materials science, but its computational cost limits its use in large-scale and high-throughput applications. While machine learning has accelerated energy prediction for specific molecular classes, transferable prediction of electron density across diverse chemical spaces remains challenging. Here, we present a universal framework based on Fourier Neural Operators (FNOs) that directly learns the mapping from external potentials to electron density distributions. Unlike conventional approaches that rely on explicit atomic orbitals, basis sets, or handcrafted descriptors, the proposed method captures global electronic interactions and long-range correlations through operator learning in the spatial-frequency domain. Trained on datasets spanning multiple elements and molecular geometries, the model achieves zero-shot generalization to entirely unseen molecular systems and accurately predicts their electron densities without retraining. This transferability arises from the intrinsic ability of FNOs to represent global structure in continuous fields. Our work establishes neural operator learning as a promising route for fast, accurate, and transferable electronic structure prediction, with potential applications in high-throughput screening and chemical space exploration.