传递神经网络的定向消息增强了图形卷积学习,用于准确预测聚合物密度.
Shenyang Sun1, Fucheng Tian2, Chenhao Zhao1
1National Synchrotron Radiation Laboratory, State Key Laboratory of Advanced Glass Materials, Anhui Provincial Engineering Research Center for Advanced Functional Polymer Films, University of Science and Technology of China, Hefei, Anhui 230029, China.
The Journal of chemical physics
|September 9, 2025
概括
机器学习准确地预测了聚合物密度,这是一个关键的材料属性. 与定向消息传递神经网络 (D-MPNNs) 结合的图形卷积神经网络 (GCNNs) 为加速聚合物发现提供了卓越的预测能力.
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:
- 聚合物密度对于材料性能和应用至关重要.
- 聚合物密度的实验性表征是耗时且昂贵的,因为化学空间广.
- 需要预测模型来有效地选潜在的聚合物结构.
研究的目的:
- 开发和评估用于准确预测聚合物密度的机器学习框架.
- 为了比较各种机器学习模型的性能,包括神经网络,随机森林,XGBoost和GCNNs.
- 为了提高模型的可解释性和理解结构-属性关系,控制聚合物密度.
主要方法:
- 利用了来自PoLyInfo数据库的1432个同聚合物的数据集.
- 实施并比较了四种机器学习模型:NNs,RF,XGBoost和GCNNs.
- 在GCNN框架内使用定向消息传递神经网络 (D-MPNN) 进行特征提取.
- 进行实验验证,并使用SHapley添加式解释 (SHAP) 进行解释.
主要成果:
- 用D-MPNN增强的GCNN模型实现了卓越的预测准确性 (MAE = 0.0497 g/cm3,R2 = 0.8097).
- 实验验证显示与GCNN预测有很强的一致性 (相对误差≤4.8%).
- SHAP分析揭示了关键的功能组对聚合物密度的贡献,提高了模型的可解释性.
结论:
- 开发的GCNN-D-MPNN框架为高通量聚合物选提供了一个可靠和可扩展的计算工具.
- 该研究提供了对结构性质关系的见解,特别是关于聚合物密度的见解.
- 这种预测能力对于推进聚合物信息学和加速新材料的发现至关重要.
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