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分子描述器对机器学习模型开发的影响,用于预测收益率的预测

Quan-De Wang¹, Lan Du², Qian Yao³

¹Jiangsu Key Laboratory of Coal-Based Greenhouse Gas Control and Utilization, Carbon Neutrality Institute and School of Chemical Engineering, China University of Mining and Technology, Xuzhou 221008, People's Republic of China.

|December 1, 2025

概括

机器学习模型准确地预测了燃料的灰尘倾向 (YSI). 使用过的Mordred描述符进行梯度提升的表现最好,有助于开发更清洁的燃料.

科学领域:

燃烧科学和化学工程的化学工程.
计算化学和材料科学计算化学和材料科学

背景情况:

煤灰倾向是燃油效率和排放的关键.
实验测量收益率索指数 (YSI) 是低效的.
机器学习 (ML) 为YSI提供了一个预测方法.

研究的目的:

将ML模型的准确性和解释性与YSI预测进行比较.
评估不同的描述符类型 (PaDEL,Mordred,QM).
确定最佳的ML模型和描述符组合,用于YSI预测.

主要方法:

开发并比较了四种ML模型:多层感知器 (MLP) 神经网络 (NN),梯度增强 (GB) 和随机森林 (RF).
使用了PaDEL,Mordred和量子力学 (QM) 描述符.
适用变特征重要性 (PFI) 过用于描述符选择.

主要成果:

根据描述符集的最佳模型变化:PaDEL的MLP,Mordred的GB,QM的RF.
在QM和完整描述符的组合中,NN表现出色;在PFI过的QM和其他描述符中,RF表现出色.
质量管理描述器为深度学习模型提供了轻微的改进.
所有模型都实现了高精度 (R2 ≈ 1.0,MAE < 20).
使用PFI过的Mordred描述符的GB显示出卓越的性能.

结论:

ML模型可以准确预测YSI,促进燃料设计.
描述器的选择显著影响了ML模型的性能.
通过PFI过的Mordred描述符与GB相结合,为YSI提供了最佳的预测准确性.

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