Tung Chi-Huan, Chang Shou-Yi, Chen Hsin-Lung, Wang Yangyang, Hong Kunlun, Carrillo Jan Michael, Sumpter Bobby G, Shinohara Yuya, Do Changwoo, Chen Wei-Ren
Department of Materials Science and Engineering, National Tsing Hua University, Hsinchu 300044, Taiwan.
Department of Chemical Engineering, National Tsing Hua University, Hsinchu 300044, Taiwan.
J Chem Phys. 2022 Apr 7;156(13):131101. doi: 10.1063/5.0086311.
We outline a machine learning strategy for quantitively determining the conformation of AB-type diblock copolymers with excluded volume effects using small angle scattering. Complemented by computer simulations, a correlation matrix connecting conformations of different copolymers according to their scattering features is established on the mathematical framework of a Gaussian process, a multivariate extension of the familiar univariate Gaussian distribution. We show that the relevant conformational characteristics of copolymers can be probabilistically inferred from their coherent scattering cross sections without any restriction imposed by model assumptions. This work not only facilitates the quantitative structural analysis of copolymer solutions but also provides the reliable benchmarking for the related theoretical development of scattering functions.
我们概述了一种机器学习策略，用于使用小角散射定量确定具有排除体积效应的AB型二嵌段共聚物的构象。通过计算机模拟的补充，在高斯过程的数学框架上建立了一个根据不同共聚物的散射特征连接其构象的相关矩阵，高斯过程是熟悉的单变量高斯分布的多元扩展。我们表明，可以从共聚物的相干散射截面概率性地推断出相关的构象特征，而不受模型假设的任何限制。这项工作不仅有助于共聚物溶液的定量结构分析，还为散射函数的相关理论发展提供了可靠的基准。
