Self-consistent field theory (SCFT), the mean-field theory of polymer thermodynamics, is a powerful tool for understanding ordered state selection in block copolymer melts and blends. However, the nonlinear governing equations pose a significant challenge when SCFT is used for phase discovery because converging an SCFT solution typically requires an initial guess close to the self-consistent solution. This Viewpoint provides a concise overview of recent efforts where machine learning methods (particle swarm optimization, Bayesian optimization, and generative adversarial networks) have been used to make the first strides toward converting SCFT from a primarily explanatory tool into one that can be readily deployed for phase discovery.

中文翻译：

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嵌段聚合物中的计算相发现


自洽场论 （SCFT） 是聚合物热力学的平均场理论，是理解嵌段共聚物熔体和共混物中有序状态选择的强大工具。然而，当 SCFT 用于相位发现时，非线性控制方程带来了重大挑战，因为收敛 SCFT 解通常需要接近自洽解的初始估计值。本观点简要概述了机器学习方法（粒子群优化、贝叶斯优化和生成对抗网络）的最新工作，这些工作已用于将 SCFT 从主要解释性工具转变为可以轻松部署用于相位发现的工具。