论文标题
机器学到了桥梁功能的近似值，改善了Ornstein-zernike方程的关闭
Machine learnt approximations to the bridge function yield improved closures for the Ornstein-Zernike equation
论文作者
论文摘要
软材料设计和粗粒度模拟的关键挑战是确定产生所需凝结相结构的组件之间的相互作用势。从理论上讲，Ornstein-cernike方程提供了一个优雅的框架来解决此反问题。液态理论中的开创性工作为该框架得出了分析封闭。但是，这些分析封闭是近似值，仅适用于特定类别的相互作用势。在这项工作中，我们将液态理论的物理学与机器学习结合在一起，直接从模拟数据中推断出封闭。所得的封闭比在广泛的相互作用电位上常用的封闭更准确。我们显示了两个原型逆设计问题的示例，拟合了粗粒的模拟潜力，我们的方法可改善一步倒置。
A key challenge for soft materials design and coarse-graining simulations is determining interaction potentials between components that give rise to desired condensed-phase structures. In theory, the Ornstein-Zernike equation provides an elegant framework for solving this inverse problem. Pioneering work in liquid state theory derived analytical closures for the framework. However, these analytical closures are approximations, valid only for specific classes of interaction potentials. In this work, we combine the physics of liquid state theory with machine learning to infer a closure directly from simulation data. The resulting closure is more accurate than commonly used closures across a broad range of interaction potentials. We show for two examples of a prototypical inverse design problem, fitting a coarse-grained simulation potential, that our approach leads to improved one-step inversion.
