可解释的机器学习模型为有机污染物和氧化基之间的反应机制提供了新的视角
Yiqiu Wu1,2, Zhixiang Wang1,2, Guangfei Yu3
1Chemistry & Chemical Engineering Data Center, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China.
Environmental science & technology
|January 8, 2025
概括
可解释的机器学习模型揭示了关键的有机结构对基反应速率的影响. 这种方法有助于预测反应机制和理解涉及活性氧物种的化学过程.
科学领域:
- 环境化学环境化学
- 计算化学的计算化学
- 化学动力学 化学动力学
背景情况:
- 机器学习 (ML) 提供了对有机结构对反应性氧物种 (ROS) 氧化影响的新见解的潜力.
- 解释ML模型仍然是一个挑战,以了解潜在的化学机制.
研究的目的:
- 开发可解释的ML模型,用于预测基 (•OH) 和有机化合物之间的二次速率常数 (k•OH).
- 确定影响这些反应速率的关键有机结构特征.
- 开发一种快速的方法来判断反应机制.
主要方法:
- 开发可解释的机器学习模型.
- 预测二阶速率常数 (k•OH).
- 无监督学习的应用是基于反应机制的有机化合物聚类.
主要成果:
- 确定了最高占成的分子轨道能量 (E_HOMO),芳香环数 (N_AR) 和碳原子数 (N_C) 作为影响k•OH的重要因素.
- 确定了k•OH和E_HOMO之间的正相关性,与电友反应有关.
- 观察到与反应部位相关的k•OH,N_AR和N_C之间的关系.
- 开发了一种聚类方法,将有机物质分为三组,以便快速判断机制.
- 将方法扩展到涉及硫酸盐基的反应.
结论:
- 可解释的ML提供了一个合理的模型,用于预测ROS氧化中的反应机制.
- 有机分子结构显著影响与基和硫酸盐基的反应动力学.
- 大数据方法提高了对环境化学中的结构-反应性关系的理解.
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