基于分子性质的机器学习方法的比较,用于基于微乳液的等效基碳数的预测
Nicholas R Furth1,2, Adam E Imel2,3, Thomas A Zawodzinski1,2,3,4
1Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville 310 Ferris Hall, 1508 Middle Drive, Knoxville, Tennessee 37996, United States.
The journal of physical chemistry. A
|August 6, 2024
概括
机器学习模型准确地预测微乳液设计的油性质. 这种方法可以加快确定等效碳数 (EACN),克服传统实验方法的局限性.
科学领域:
- 物理化学 物理化学
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 水性-脂性平衡 (HLB) 对于设计微乳液系统至关重要.
- 相当碳数 (EACN) 是一个关键参数,可以预测微乳液相位的行为,它取决于油的性质.
- 实验EACN的确定是耗时的,需要特定的条件,并且对杂质敏感.
研究的目的:
- 开发和比较机器学习 (ML) 模型,以便快速可靠地预测EACN.
- 评估使用分子结构的ML模型的几何优化的必要性.
- 评估图形神经网络 (GNN) 和XGBoost用于EACN预测的性能.
主要方法:
- 使用简化分子输入线路输入系统 (SMILES) 代码作为GNN和XGBoost的输入.
- 将三个GNN架构与一个XGBoost模型进行比较.
- 研究了分子描述符与ML模型中的几何优化对分子描述符的影响.
主要成果:
- 使用默克分子力场-94的晶体图卷积神经网络 (CGCNN) 实现了最佳性能.
- 最好的模型显示出1.15个EACN单位的误差,用于未知数据.
- 该模型实现了0.9的高确定系数 (R2) 评分,表明强大的预测能力.
结论:
- 机器学习模型,特别是GNN,为实验EACN确定提供了更快,更可靠的替代方案.
- 基于分子结构的输入 (SMILES) 对EACN预测有效,CGCNN显示有希望.
- 该研究强调了ML在加速微乳液系统的设计和优化方面的潜力.
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