发表机构
University of Science and Technology of China; Suzhou Institute for Advanced Research, University of Science and Technology of China; Suzhou Big Data & AI Research and Engineering Center（中国科学技术大学; 中国科学技术大学苏州高等研究院; 苏州大数据与人工智能研究与工程中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究开发了一种保结构神经密度泛函，可准确预测聚合物电解质的离子结构与响应，具备空间可迁移性，性能优于同类架构。
AI 中文摘要
预测非均匀聚合物电解质的结构与响应，需要描述离子关联时兼顾分子尺度精度，同时在空间尺度和几何结构间具备可迁移性。我们开发了一种电解质神经密度泛函，它保留空间对称性、热力学可积性和诺特定理，在稳定的非临界体相状态下可恢复完美屏蔽效应。其非线性密度依赖关系捕捉了浓度依赖的关联，这是对闭对近似的补充，包括强耦合下长波长数涨落从增强到抑制的转变。该泛函描述未训练盐浓度下的密度分布，预测体相结构因子和长波长数响应。仅基于分子动力学的平面密度和内力分布训练，该泛函可预测更大区域及二维外场下的离子结构。在相同离子数据上，它比其他三种神经密度泛函架构更准确，且在训练运行次数仅为四分之一时仍保持精度，而最优替代方案的误差增长约三分之二。空间可迁移性为将分子关联与更大尺度的连续介质预测建立联系提供了必要基础。
英文摘要
Predicting the structure and response of inhomogeneous polymer electrolytes requires a description of ion correlations that retains molecular-scale accuracy while remaining transferable across spatial scales and geometries. We develop a neural density functional for electrolytes that preserves spatial symmetries, thermodynamic integrability and the Noether identities, with perfect screening recovered in stable, noncritical bulk states. Its nonlinear density dependence captures the concentration-dependent correlations missed by a pair closure, including a crossover from enhanced to suppressed long-wavelength number fluctuations at strong coupling. The functional describes density profiles at an untrained salt concentration and predicts bulk structure factors and the long-wavelength number response. Trained solely on planar density and internal-force profiles from molecular dynamics, the functional predicts ionic structure in larger domains and in two-dimensional external fields. On the same ion data, it is more accurate than three other neural density-functional architectures and keeps its accuracy with a quarter of the training runs, where the errors of the best alternative grow by about two thirds. The spatial transferability provides a necessary foundation for connecting molecular correlations to continuum predictions at larger scales.
