使用机器学习阐明反应性物种在微污染物降解中的作用
Xinjie Song1, Jing Zhao2, Mingkai Jia1
1State Key Laboratory of Water Pollution Control and Green Resource Recycling, School of the Environment, Nanjing University, Nanjing 210023, China; Institute for the Environment and Health, Nanjing University Suzhou Campus, Suzhou 215163, China.
Water research
|January 17, 2026
概括
机器学习模型预测微污染物被反应性物种 (RNS) 降解. 该框架有助于设计先进的水处理策略,通过识别反应性结构和预测反应速率.
科学领域:
- 环境化学环境化学
- 水处理技术水处理技术
- 计算化学计算化学
背景情况:
- 反应性物种 (RNS) 在水系统中扮演着被低估的角色.
- 用RNS对微污染物反应性的实验评估是耗时的.
研究的目的:
- 开发机器学习模型来预测微污染物对RNS的反应性.
- 建立RNS降解的定量结构-反应关系.
- 为水处理应用提供预测工具.
主要方法:
- 利用分子访问系统 (MACCS) 和摩根指纹 (MF) 来描述微污染物结构.
- 采用机器学习算法,包括XGBoost和贝叶斯优化用于超参数调整.
- 对关键微污染物的实验数据进行验证的模型预测.
主要成果:
- 使用MACCS指纹的XGBoost模型显示出高的预测准确性 (R2train = 0.9140,R2test = 0.8871).
- 确定了负责与RNS的反应性的关键结构部分.
- 成功预测了413种额外的微污染物的反应速率常数.
结论:
- 开发并验证了RNS与微污染物相互作用的预测ML框架.
- 该模型增强了对水中的RNS降解途径的理解.
- 创建了一个可访问的网络界面,用于水处理设计中的实际应用.
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