标题： 基于散射的梯形聚合物结构机器学习辅助表征 显示英文标题

标题： Machine Learning-Assisted Profiling of Ladder Polymer Structure using Scattering

摘要： 梯形聚合物因其刚性梯形结构而闻名，表现出卓越的热稳定性和机械强度，使其成为先进应用的候选材料。 然而，从溶液散射中准确确定其结构仍然是一个挑战。 它们的链构象主要由单体的固有取向特性和它们的相对取向所决定，导致弯曲角度呈现双峰分布，这与传统聚合物链的弯曲角度遵循单峰高斯分布不同。 同时，传统的聚合物链散射模型并未考虑这些独特的结构特征。 本研究引入了一种新方法，将机器学习与蒙特卡洛模拟相结合，以解决这一挑战。 我们首先开发了一种蒙特卡洛模拟，用于采样梯形聚合物的构型空间，其中每个单体被建模为双轴段。 然后，我们建立了一个基于高斯过程回归的机器学习辅助散射分析框架。 最后，我们在一种梯形聚合物溶液上进行了小角中子散射实验，以应用我们的方法。 我们的方法揭示了传统方法无法捕捉到的梯形聚合物的结构细节。 显示英文摘要

摘要： Ladder polymers, known for their rigid, ladder-like structures, exhibit exceptional thermal stability and mechanical strength, positioning them as candidates for advanced applications. However, accurately determining their structure from solution scattering remains a challenge. Their chain conformation is largely governed by the intrinsic orientational properties of the monomers and their relative orientations, leading to a bimodal distribution of bending angles, unlike conventional polymer chains whose bending angles follow a unimodal Gaussian distribution. Meanwhile, traditional scattering models for polymer chains do not account for these unique structural features. This work introduces a novel approach that integrates machine learning with Monte Carlo simulations to address this challenge. We first develop a Monte Carlo simulation for sampling the configuration space of ladder polymers, where each monomer is modeled as a biaxial segment. Then, we establish a machine learning-assisted scattering analysis framework based on Gaussian Process Regression. Finally, we conduct small-angle neutron scattering experiments on a ladder polymer solution to apply our approach. Our method uncovers structural details of ladder polymers that conventional methods fail to capture.