Machine Learning-Assisted Profiling of Ladder Polymer Structure using Scattering
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866908373741469696 |
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| author | Ding, Lijie Tung, Chi-Huan Cao, Zhiqiang Ye, Zekun Gu, Xiaodan Xia, Yan Chen, Wei-Ren Do, Changwoo |
| author_facet | Ding, Lijie Tung, Chi-Huan Cao, Zhiqiang Ye, Zekun Gu, Xiaodan Xia, Yan Chen, Wei-Ren Do, Changwoo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00134 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine Learning-Assisted Profiling of Ladder Polymer Structure using Scattering Ding, Lijie Tung, Chi-Huan Cao, Zhiqiang Ye, Zekun Gu, Xiaodan Xia, Yan Chen, Wei-Ren Do, Changwoo Soft Condensed Matter Materials Science Computational Physics 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. |
| title | Machine Learning-Assisted Profiling of Ladder Polymer Structure using Scattering |
| topic | Soft Condensed Matter Materials Science Computational Physics |
| url | https://arxiv.org/abs/2411.00134 |