ChargeFlow：基于流匹配的电荷条件电子密度精修
ChargeFlow: Flow-Matching Refinement of Charge-Conditioned Electron Densities
AI总结：
本文提出ChargeFlow模型，通过3D U-Net速度场将原子密度叠加转换为DFT电子密度，提升电荷态下电子密度计算精度，尤其在非局域电荷重分布和电荷态外推问题中表现突出。
摘要：
准确的电荷密度对于电子结构理论至关重要，但使用密度泛函理论计算电荷状态依赖的密度在大规模筛选和缺陷工作流程中仍过于昂贵。我们提出了ChargeFlow，一种基于流匹配的精修模型，通过3D U-Net速度场将电荷条件下的原子密度叠加转换为对应的DFT电子密度，使用原生周期性实空间网格。在9,502个带电材料项目衍生计算上训练，并在外部1,671个结构基准上评估，涵盖钙钛矿、带电缺陷、钻石缺陷、金属有机框架和有机晶体。ChargeFlow在非均匀分布的类别中并非最佳，但在非局域电荷重分布和电荷态外推问题中表现最强，将变形密度误差从3.62%降低到3.21%，电荷响应余弦相似度从0.571提高到0.655，相对于ResNet基线。预测的密度在下游分析中仍具有化学实用性，成功对所有1,671个基准结构进行了Bader划分，并产生高保真度的静电势，使流匹配成为带电材料的实用密度精修策略。
英文摘要：
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 downstream analysis, yielding successful Bader partitioning on all 1,671 benchmark structures and high-fidelity electrostatic potentials, which positions flow matching as a practical density-refinement strategy for charged materials.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
