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基于机器学习和分子动力学模拟的聚胺玻璃过渡温度的预测和解释性研究

Wenjia Huo¹, Boyang Liang¹, Xiang Wu¹

¹School of Petrochemical Engineering, Shenyang University of Technology, Liaoyang 111003, China.

|August 14, 2025

概括

机器学习模型使用大型数据集准确预测聚胺玻璃过渡温度 (T_g). 这种方法加速了材料设计,为分子动力学模拟提供了更快的替代方案.

科学领域:

材料科学材料科学材料科学
计算化学的计算化学
聚合物科学聚合物科学

背景情况:

机器学习 (ML) 为发现高性能材料提供了新的途径.
玻璃过渡温度 (T_g) 是聚胺 (PI) 的一个关键性质.
有限的数据集阻碍了ML模型在预测材料属性的准确性.

研究的目的:

开发一个准确的定量结构-属性关系 (QSPR) 模型来预测PI T_g.
确定影响T_g的关键结构描述因素.
通过分子动力学 (MD) 模拟来验证ML预测.

主要方法:

组装了一个1261个聚胺的数据集.
采用了九个回归算法,包括分类提升,用于QSPR建模.
使用了SHapley添加式解释 (SHAP) 来进行描述器重要性分析.
进行全原子分子动力学 (MD) 模拟以进行验证.

主要成果:

分类提升实现了最高的准确性,在测试组中R² = 0.895.
确定NumRotatableBonds描述符对T_g有显著的负面影响.
ML的预测与MD的模拟显示出很好的一致性 (最低偏差为6.75%).

结论:

广泛的数据集对于训练材料科学中准确的ML模型至关重要.
与MD模拟相比,开发的ML框架显著加速了财产预测.
这种可解释的ML方法是设计具有向T_g的聚合物的一种有价值的工具.

关键词:

玻璃过渡温度是玻璃过渡温度.机器学习是机器学习.分子动力学模拟模拟聚胺聚胺的使用方法

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