用基于结构的描述符和机器学习预测alkoxy根对的系统间交叉速率常数
Rashid R Valiev1, Rinat T Nasibullin1, Hilda Sandström2
1Department of Chemistry, University of Helsinki, P.O. Box 55 (A.I. Virtanens plats 1), FIN-00014, Finland. valievrashid@gmail.com.
Physical chemistry chemical physics : PCCP
|July 4, 2025
概括
机器学习模型准确地预测过氧基的系统间交叉速率常数,这对于理解二次有机气溶形成至关重要. 这为计算密集的量子化学计算提供了更快的替代方案.
科学领域:
- 大气化学 大气化学
- 计算化学计算化学
- 机器学习应用 机器学习应用
背景情况:
- 过氧基 (RO2) 是氧化过程中的关键大气中间体.
- 过氧基的反应形成过氧化物,有助于二次有机气溶 (SOA) 的形成.
- 准确估计系统间交叉 (ISC) 速率常数对于大气模型至关重要,但在计算上昂贵.
研究的目的:
- 开发和评估机器学习模型,用于预测酒氧基对中的ISC速率常数.
- 为ISC速率估计提供一种更快,更可扩展的替代传统量子化学方法.
主要方法:
- 创建了一个数据集,包含98,082个根对构造及其速率常数.
- 应用了三个机器学习模型:随机森林 (RF),CatBoost (CB) 和神经网络 (NN).
- 使用分子几何描述符作为ML模型的输入特征来预测T1 → Si ISC速率常数.
主要成果:
- 所有的ML模型都达到一个数量级内的平均绝对误差 (MAE),R2>0.82.
- ML预测通常与量子化学计算保持在1-2个数量级之内.
- 开发的模型提供了一个快速和可扩展的方法来估计ISC速率常数.
结论:
- 机器学习为估计大气化学中的ISC速率常数提供了一种可行和高效的方法.
- 这些ML模型可以支持对SOA形成开发更可靠的计算模型.
- 这项研究表明了ML在加速复杂化学过程建模方面的潜力.
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