2024/10/31 by Lijie Ding, Chi-Huan Tung, Ding, Lijie +12 · 4 citations
Computer Science · Engineering · Materials Science · #Advanced Data Processing Techniques #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Handwritten Text Recognition Techniques #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Soft Condensed Matter (cond-mat.soft)
paper · pdf · doi:10.48550/arxiv.2411.00134
openalex publication_date 2024/10/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.