双嵌入:一个微调的语言模型方法,用于准确的聚合物玻璃过渡温度预测
Aymar Tchagoue1,2, Véronique Eglin1, Jean-Marc Petit1
1INSA Lyon, UCBL, CNRS, LIRIS UMR 5205, 69100 Villeurbanne, France.
Journal of chemical information and modeling
|November 4, 2025
概括
预测聚合物特性,如玻璃过渡温度 (Tg) 是一个挑战. 一种新的双嵌入方法通过将矢量相似性与实际属性值对齐来改进Tg预测,优于现有模型.
科学领域:
- 聚合物科学 聚合物科学
- 材料 信息学 信息学
- 机器学习 机器学习
背景情况:
- 聚合物信息学利用语言模型 (例如BERT) 来进行属性预测.
- 目前使用嵌入式的方法主要捕捉化学结构,而不是物理化学特性,如玻璃过渡温度 (Tg).
- 准确预测Tg仍然是聚合物科学中的一个重大挑战.
研究的目的:
- 开发一个增强的双嵌入框架,以改善Tg预测.
- 创建嵌入式,其中向量相似性与Tg值直接相关.
- 推进聚合物信息学超越结构为中心的方法.
主要方法:
- 引入了双嵌入框架,将标准BERT嵌入与微调的BERT模型相结合.
- 微调模型被明确训练,以确保向量相似性反映Tg值的近距离.
- 评估了四个不同的数据集的框架,包括异质聚合物,同聚合物和聚胺,与25个机器学习基线进行比较.
主要成果:
- 与标准BERT嵌入相比,双嵌入方法显著提高了Tg预测准确性.
- 在Tg预测中实现了高达20%的根平均平方误差 (RMSE) 降低.
- 在多个基准指标中表现优于其他方法,包括基于图形和基于描述者的方法.
结论:
- 将分子特性直接嵌入到表示中可以提高聚合物信息学中的预测准确性.
- 双嵌入框架提供了一种更有效的方法来预测像Tg.这样的聚合物特性.
- 这项工作标志着在聚合物数据科学中朝着属性意识表示的方向迈进.
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