可迁移三维经典密度泛函的等变学习

中文摘要 AI 辅助

液体表现出的集体行为对热力学条件、界面和限域环境极为敏感，但预测每种新状态通常需要单独的原子模拟。经典密度泛函理论提供了可复用的变分描述，但其核心的超额自由能泛函通常是未知的，而已有的学习近似方法大多局限于平面或更低维的设置。本文展示，可直接从完全三维的平衡密度场中学习该泛函，同时保留空间对称性和变分一致性，无需自由能或化学势标签。单个学习得到的泛函可跨温度、系统尺寸和统计系综迁移，且能恢复结构因子、状态方程、液-气共存及界面展宽，这些均未被用作训练目标。将其应用于复杂三维几何时，可预测与胶体间溶剂耗尽桥的形成和断裂相关的非单调力，以及在相互连通的螺旋状孔中的吸附。这些结果表明，平衡密度数据可转化为可迁移的热力学生成器，连接微观液体结构与响应、相行为及集体现象。

英文摘要

Liquids exhibit collective behavior that depends sensitively on thermodynamic conditions, interfaces and confinement, yet predicting each new state commonly requires a separate atomistic simulation. Classical density functional theory offers a reusable variational description, but its central excess free-energy functional is generally unknown, and learned approximations have largely remained restricted to planar or lower-dimensional settings. Here we show that this functional can be learned directly from fully three-dimensional equilibrium density fields while preserving spatial symmetry and variational consistency, without free-energy or chemical-potential labels. A single learned functional transfers across temperatures, system sizes and statistical ensembles, and recovers structure factors, the equation of state, liquid--vapor coexistence and interfacial broadening, none of which are used as training targets. Applied to complex three-dimensional geometries, it predicts the non-monotonic force associated with formation and rupture of a solvent-depleted bridge between colloids and adsorption in an interconnected gyroid pore. These results demonstrate that equilibrium density data can be converted into a transferable thermodynamic generator connecting microscopic liquid structure to response, phase behavior and collective phenomena.

发表机构

• UC Berkeley（加州大学伯克利分校）
• Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）
• Bakar Institute of Digital Materials for the Planet, UC Berkeley（加州大学伯克利分校地球数字材料巴卡尔研究所）

机构由 AI 辅助整理，请以论文原文为准。