高保真与低保真实验数据在机器学习模型性能中的作用,用于预测聚合物可溶性
Mona Amrihesari1, Manali Banerjee2, Raul Olmedo1
1School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.
Macromolecular rapid communications
|July 28, 2025
概括
高准确度的实验数据显著改善了用于预测聚合物可溶性的机器学习模型. 量化测量优于视觉检查,用于训练聚合物科学中准确的AI工具.
科学领域:
- 聚合物科学 聚合物科学
- 材料科学 材料科学 材料科学
- 计算化学计算化学
背景情况:
- 聚合物与溶剂的兼容性对于开发新材料和配方至关重要.
- 机器学习 (ML) 和人工智能 (AI) 显示出预测聚合物可溶性的前景.
- 模型的性能取决于培训数据的质量和性质.
研究的目的:
- 评估实验数据的真实性如何影响ML模型对聚合物-溶剂兼容性的性能.
- 为了比较训练在高保真度 (基于度) 和低保真度 (视觉检查) 数据集上的ML分类器.
- 确定影响基于ML的可溶性预测准确性的关键因素.
主要方法:
- 创建了两个数据集:来自度测量的高保证度 (Crystal16) 和来自视觉检查的低保证度.
- 聚合物使用一次热编码进行编码,溶剂使用摩根指纹进行编码.
- 使用XGBoost分类器预测可溶性标签 (可溶性,不可溶性,部分可溶性).
主要成果:
- 在高可靠性数据上训练的模型表现出卓越的性能,更好地捕捉部分可溶性行为.
- 与主观视觉检查相比,定量测量导致了更清晰的阶级区别.
- 包括温度作为一个特征,提高了低保真数据集的预测准确度.
结论:
- 实验数据的严谨性对于开发聚合物科学中可概括的ML工具至关重要.
- 高准确度的定量数据来源对于可靠的AI驱动的聚合物可溶性预测至关重要.
- 在利用文献衍生数据集时,考虑温度等实验参数至关重要.
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