标题： 学习液-液相分离的体相和界面物理特性 显示英文标题

标题： Learning the bulk and interfacial physics of liquid-liquid phase separation

摘要： 我们使用基于仿真的监督机器学习和经典密度泛函理论来研究二元混合物相共存相关的体相和界面现象。 对于一个典型的对称性Lennard-Jones混合物，我们训练的神经密度泛函能够准确得出液-液和液-气双线，并对相关界面张力在整个流体相图中的变化进行预测。 从后者我们可以确定三相共存线上的流体-流体界面处的接触角，并确认在这种对称混合物中不可能存在润湿转变。 显示英文摘要

摘要： We use simulation-based supervised machine learning and classical density functional theory to investigate bulk and interfacial phenomena associated with phase coexistence in binary mixtures. For a prototypical symmetrical Lennard-Jones mixture our trained neural density functional yields accurate liquid-liquid and liquid-vapour binodals together with predictions for the variation of the associated interfacial tensions across the entire fluid phase diagram. From the latter we determine the contact angles at fluid-fluid interfaces along the line of triple-phase coexistence and confirm there can be no wetting transition in this symmetrical mixture.