Machine learning of the architecture-property relationship in graft polymers

IF 2.9 3区化学 Q3 CHEMISTRY, PHYSICAL

Physical Chemistry Chemical Physics Pub Date : 2025-06-10 DOI:10.1039/d5cp01334h

Kevin V. Bigting, Jordan J. Carden, Shubhadeep Nag, Jimmy Lawrence, Yen-Fang Su, Yaxin An

引用次数: 0

Abstract

Bottlebrush polymers, a special class of comb polymers, are promising in energy and biomedical applications. However, the diverse architectures make it challenging to establish their structure-property relationships. We systematically investigate how backbone and side-chain architectures influence four key properties: glass transition temperature (Tg), self-diffusion coefficient (D), radius of gyration (Rg ), and packing density (ρ ). Using molecular dynamics simulations, we analyze a dataset of 500 comb polymers with randomly positioned side chains. Tg and D exhibit complicated relationships with the architectures, beyond the empirical prediction for linear polymers. To effectively capture nonlinear structure-property relationships, we develop dense neural networks (DNNs) and convolutional neural networks (CNNs).

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接枝聚合物结构-性质关系的机器学习

瓶刷聚合物是一种特殊的梳状聚合物，在能源和生物医学领域有着广阔的应用前景。然而，不同的体系结构使得建立它们的结构-属性关系变得具有挑战性。我们系统地研究了主链和侧链结构如何影响四个关键性质：玻璃化转变温度（Tg）、自扩散系数(D)、旋转半径（Rg）和堆积密度（ρ）。利用分子动力学模拟，我们分析了500个具有随机侧链的梳状聚合物的数据集。Tg和D表现出复杂的结构关系，超出了线性聚合物的经验预测。为了有效地捕获非线性结构-性质关系，我们开发了密集神经网络（dnn）和卷积神经网络（cnn）。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Physical Chemistry Chemical Physics 化学-物理：原子、分子和化学物理

CiteScore

5.50

自引率

9.10%

发文量

2675

审稿时长

2.0 months

期刊介绍： Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.