关于"推进材料属性预测:使用基于物理的机器学习模型来确定粘度"的评论
Maximilian Fleck1, Samir Darouich2,3, Marcelle B M Spera1
1Institute of Thermodynamics and Thermal Process Engineering, University of Stuttgart, Pfaffenwaldring 9, 70569, Stuttgart, Germany.
Journal of cheminformatics
|August 28, 2025
概括
当数据稀缺时, 基于物理的机器学习可以改善粘度预测. 整合分子动力学描述器,如蒸发热, 提高纯液体的准确性.
科学领域:
- 物理化学
- 材料科学
- 计算化学
背景情况:
- 数据驱动的机器学习 (ML) 面临着有限数据的属性预测挑战.
- 整合基于物理的模型与机器学习可以提高预测性能.
- 和其他人. 在定量结构-属性关系 (QSPR) 中使用分子动力学 (MD) 描述器来预测液体粘度.
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