通过机器学习模型预测OH/HO2和基之间的抽象反应的速率常数
Min Xia1,2, Yu Zhang2, Hongwei Song2
1College of Physical Science and Technology, Central China Normal University, Wuhan 430079, China.
机器学习准确地预测了抽象反应的燃料氧化率常数. XGB-FNN模型显示了强度,区分了不同基位点的反应性.
科学领域:
- 燃烧化学 燃烧化学是什么
- 计算化学计算化学
- 化学动力学 化学动力学
背景情况:
- 吸收反应在燃料氧化过程中至关重要.
- 速率常数的实验和理论确定是具有挑战性的,因为高的激素反应性.
- 机器学习为预测这些速率常数提供了一个可行的替代方案.
研究的目的:
- 开发和评估机器学习模型,用于预测抽象反应的速率常数.
- 调查XGB,FNN和混合XGB-FNN方法的性能.
- 评估模型预测涉及较大的基的反应的能力,以及在不同地点区分反应能力.
主要方法:
- 使用了三个机器学习算法:XGBoost (XGB),Feedforward神经网络 (FNN) 和混合XGB-FNN.
- 根据相关系数,描述器重要性和聚类热图,选择了六个关键描述器.
- 训练并测试了和基 (OH) 或基 (HO2) 基之间的抽取反应的速率常数的模型.
主要成果:
- 混合型XGB-FNN模型展示了最强大的性能.
- 对于子+OH反应的平均偏差为89.13%,对于子+HO2反应的平均偏差为190.93%.
- 该模型对较大的基具有外推能力,并且可以根据原子抽象地点区分反应性.
结论:
- 在XGB-FNN模型是有效的预测热速常数的抽象反应在燃料氧化.
- 开发的模型显示了理解复杂的燃烧化学和指导实验努力的希望.
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