V2Rho-FNO: Fourier Neural Operator for Electronic Density Prediction
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866911520765509632 |
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| author | Jin, Yingdi Qin, Xinming Liu, Ruichen Liu, Jie Li, Zhenyu Yang, Jinlong |
| author_facet | Jin, Yingdi Qin, Xinming Liu, Ruichen Liu, Jie Li, Zhenyu Yang, Jinlong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_15669 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | V2Rho-FNO: Fourier Neural Operator for Electronic Density Prediction Jin, Yingdi Qin, Xinming Liu, Ruichen Liu, Jie Li, Zhenyu Yang, Jinlong Chemical Physics Quantum Physics 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. |
| title | V2Rho-FNO: Fourier Neural Operator for Electronic Density Prediction |
| topic | Chemical Physics Quantum Physics |
| url | https://arxiv.org/abs/2603.15669 |