转移学习增强预测玻璃过渡温度在基于比斯马利胺的聚胺中
Ziqi Wang1,2,3, Yu Liu1,2,3, Xintong Xu1,2,3
1School of Materials Science and Engineering, Beihang University, Beijing 100191, China.
Polymers
|July 12, 2025
概括
转移学习增强了使用深度神经网络对双胺聚胺 (BMI) 的玻璃过渡温度 (Tg) 预测. 这种方法克服了数据稀缺性,识别了影响材料设计Tg的关键分子因素.
科学领域:
- 材料科学 材料科学 材料科学
- 聚合物化学 聚合物化学
- 计算化学的计算化学
背景情况:
- 玻璃过渡温度 (Tg) 对于基于双胺的聚胺 (BMI) 树脂特性至关重要.
- 有限的实验数据阻碍了BMI系统中Tg的准确预测建模.
研究的目的:
- 开发一种混合建模框架,用于预测BMI树脂中的Tg.
- 利用转移学习和可解释的机器学习来解决数据稀缺问题.
- 确定影响Tg.的关键分子描述因素.
主要方法:
- 一个多层感知器 (MLP) 深度神经网络在一个大型聚合物数据库上进行了预训练,并在一个小型的BMI数据集上进行了微调.
- 六个可解释的机器学习算法用于透明的预测建模.
- 沙普利添加式解释 (SHAP) 分析量化了描述器对Tg的贡献.
主要成果:
- 转移学习方法在数据稀缺的BMI场景中显著提高了Tg的预测准确性.
- SHAP分析显示,电荷分布的不均性,分子拓和表面积是Tg.的主要影响因素.
- 混合框架为高性能BMI树脂的分子设计提供了洞察力.
结论:
- 转移学习提供了一个强大的解决方案,用于在BMI树脂中预测Tg的模型,但数据有限.
- 了解分子描述者的影响,使先进的BMI材料的合理设计成为可能.
- 这项研究通过集成建模为设计高性能BMI树脂奠定了基础.
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