arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2004.09114cond-mat.softphysics.comp-ph
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桥函数的机器学习近似产生了Ornstein-Zernike方程的改进闭包

Machine learnt approximations to the bridge function yield improved closures for the Ornstein-Zernike equation

Rhys E. A. Goodall, Alpha A. Lee

摘要:

软材料设计和粗粒化模拟的一个关键挑战是确定产生所需凝聚相结构的组分之间的相互作用势。理论上,Ornstein-Zernike方程为解决这个逆问题提供了一个优雅的框架。开创性的工作,在液态理论推导出的框架分析关闭。然而,这些解析闭合是近似的,仅对特定类别的相互作用势有效。在这项工作中,我们将液态理论的物理学与机器学习相结合,直接从模拟数据中推断出一个闭包。在广泛的相互作用潜力范围内,所得到的闭合比常用的闭合更准确。我们展示了两个典型的反设计问题的例子,拟合粗粒度的模拟潜力,我们的方法导致改进的一步反演。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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