arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2402.19019cond-mat.soft
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基于机器学习的两亲分子自组装介观结构探索

Machine learning-enabled exploration of mesoscale architectures in amphiphilic-molecule self-assembly

Takeo Sudo, Satoki Ishiai, Yuuki Ishiwatari, Takahiro Yokoyama, Kenji Yasuoka, Noriyoshi Arai

AI总结:

本研究针对两亲分子自组装介观结构评估流程复杂的问题,提出用粒子类型与坐标数据训练图神经网络提取局部结构的方法,在含数百个结构、最多4050个粗粒化粒子的模拟中实现最高78.35%的分类准确率,无需人工特征工程,在材料科学领域具应用潜力。

摘要:

两亲分子会根据其分子结构、温度及其他物理条件自发形成多种形状的自组装结构。这些结构的功能由其形态决定,且必须通过分子模拟评估其性质以实现可复现。然而,评估此类复杂结构涉及诸多流程步骤。本研究探究了机器学习模型从介观无序自组装结构中提取结构特征的潜力,并提出一种方法:利用粒子类型和坐标数据训练用于自组装结构结构分析的机器学习模型。在该方法中,采用图神经网络提取局部结构数据以进行分析。在使用多达4050个粗粒化粒子的数百个自组装结构开展的模拟中,该方法成功提取并分类了局部结构,准确率最高达78.35%。由于机器学习模型无需人工特征工程即可学习结构特征,所提出的方法在材料科学领域具有重要的应用潜力。

英文摘要:

Amphiphilic molecules spontaneously form self-assembled structures of various shapes depending on their molecular structures, the temperature, and other physical conditions. The functionalities of these structures are dictated by their formations and their properties must be evaluated for reproduction using molecular simulations. However, the assessment of such intricate structures involves many procedural steps. This study investigates the potential of machine-learning models to extract structural features from mesoscale non-ordered self-assembled structures, and suggests a methodology in which machine-learning models for the structural analysis of self-assembled structures are trained on particle types and coordinate data. In the proposed approach, graph neural networks are utilised to extract local structural data for analysis. In simulations using several hundred self-assembled structures of up to 4050 coarse-grained particles, local structures are successfully extracted and classified with up to 78.35 % accuracy. As the machine-learning models learn structural characteristics without the need for human-made feature engineering, the proposed method has important potential applications in the field of materials science.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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