arXiv：cs.LG· Liyao Lyu·· 3 小时前
面向聚合物电解质离子的结构保持神经密度泛函
A structure-preserving neural density functional for the ions of a polymer electrolyte
AI 导读
研究者提出一种保持空间对称性、热力学可积性与 Noether 恒等式的神经密度泛函，用于描述聚合物电解质中的离子关联，并在稳定非临界体相中恢复完美屏蔽。该泛函的非线性密度依赖捕捉到对关联闭包遗漏的浓度依赖关联，包括强耦合下长波数涨落由增强转为抑制的转变，并能在未训练的盐浓度下描述密度分布、预测体相结构因子。
正文
Abstract: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.
| Subjects: | Soft Condensed Matter (cond-mat.soft); Machine Learning (cs.LG); Numerical Analysis (math.NA) |
| Cite as: | arXiv:2610.12132 [cond-mat.soft] |
| (or arXiv:2610.12132v1 [cond-mat.soft] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12132 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Liyao Lyu [view email]
[v1]
Thu, 8 Oct 2026 15:22:38 UTC (465 KB)
来源：arXiv：cs.LG · arxiv.org