[2603.23943] ChargeFlow: Flow-Matching Refinement of Charge-Conditioned Electron Densities

[2603.23943] ChargeFlow: Flow-Matching Refinement of Charge-Conditioned Electron Densities

arXiv - Machine Learning 3 min read

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Abstract page for arXiv paper 2603.23943: ChargeFlow: Flow-Matching Refinement of Charge-Conditioned Electron Densities

Condensed Matter > Materials Science arXiv:2603.23943 (cond-mat) [Submitted on 25 Mar 2026] Title:ChargeFlow: Flow-Matching Refinement of Charge-Conditioned Electron Densities Authors:Tri Minh Nguyen, Sherif Abdulkader Tawfik, Truyen Tran, Svetha Venkatesh View a PDF of the paper titled ChargeFlow: Flow-Matching Refinement of Charge-Conditioned Electron Densities, by Tri Minh Nguyen and 3 other authors View PDF HTML (experimental) Abstract:Accurate charge densities are central to electronic-structure theory, but computing charge-state-dependent densities with density functional theory remains too expensive for large-scale screening and defect workflows. We present ChargeFlow, a flow-matching refinement model that transforms a charge-conditioned superposition of atomic densities into the corresponding DFT electron density on the native periodic real-space grid using a 3D U-Net velocity field. Trained on 9,502 charged Materials Project-derived calculations and evaluated on an external 1,671-structure benchmark spanning perovskites, charged defects, diamond defects, metal-organic frameworks, and organic crystals, ChargeFlow is not uniformly best on every in-distribution class but is strongest on problems dominated by nonlocal charge redistribution and charge-state extrapolation, improving deformation-density error from 3.62% to 3.21% and charge- response cosine similarity from 0.571 to 0.655 relative to a ResNet baseline. The predicted densities remain chemically useful under...

Originally published on March 26, 2026. Curated by AI News.

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