基于结构的反应描述器用于通过机器学习预测速率常数:应用到CH3/H/O激素从基中抽取
Yu Zhang1,2, Jinhui Yu2,3, Hongwei Song2
1College of Physical Science and Technology, Huazhong Normal University, Wuhan 430079, China.
机器学习使用新型分子描述器准确预测燃烧反应速率常数. XGB-FNN模型对抽取反应具有很高的准确性,优于依赖激活能量的方法.
科学领域:
- 计算化学是一种计算化学.
- 化学动力学 化学动力学
- 机器学习应用程序 机器学习应用程序
背景情况:
- 确定燃烧反应的热速常数在实验和理论上是具有挑战性的.
- 机器学习 (ML) 为预测反应速率常数提供了一种强大的方法.
研究的目的:
- 开发定量结构-属性关系 (QSPR) 模型,用于估计抽象反应的速率常数.
- 使用新型分子描述器评估XGB,FNN和XGB-FNN算法的性能.
主要方法:
- 使用了三个监督的ML算法:XGBoost (XGB),Feedforward神经网络 (FNN) 和混合XGB-FNN.
- 利用摩根分子指纹和拓指数来表示化学反应.
- 开发了涉及和自由基 (CH3,H,O) 的抽象反应的QSPR模型.
主要成果:
- 混合XGB-FNN模型的平均偏差为65.4% (基 + CH3),12.1% (基 + H) 和64.5% (基 + O).
- 性能与使用激活能量作为描述符的模型相比或优于这些模型,避免了昂贵的初始计算.
- XGB-FNN模型表现出良好的概括能力,准确预测不同抽象站点和异构体的速率常数.
结论:
- 新型分子描述符与ML结合,特别是XGB-FNN,为预测燃烧反应速率常数提供了准确有效的方法.
- 拟议的描述符显示了在各种化学反应的ML建模中更广泛应用的潜力.
- 这种方法为需要复杂计算的传统方法提供了可行的替代方案.
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