Insight into the defluorination ability of per- and polyfluoroalkyl substances based on machine learning and quantum chemical computations.
Insight into the defluorination ability of per- and polyfluoroalkyl substances based on machine learning and quantum chemical computations.
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基于机器学习和量子化学计算深入了解全氟烷基和多氟烷基物质的脱氟能力。
DOI：
10.1016/j.scitotenv.2021.151018
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发表时间：
2022
影响因子：
9.8
通讯作者：
Liang Yong
中科院分区：
文献类型：
--
作者：
Cao Huiming;Peng Jianhua;Zhou Zhen;Sun Yuzhen;Wang Yawei;Liang Yong
紫外线产生的水合电子在多氟烷基物质和全氟烷基物质 (PFAS) 的脱氟反应中发挥着关键作用。然而，有限的实验数据阻碍了对新兴 PFAS 结构特征对其脱氟能力影响的深入了解。因此，在本研究中，我们采用基于机器学习算法的数量构效关系模型来开发PFAS相对脱氟能力的预测模型。使用五倍交叉验证对模型进行超参数调整，这表明以 PaDEL 描述符作为最佳模型的梯度增强算法具有优异的预测性能（R2test= 0.944 和 RMSEtest= 0.114）。描述符的重要性表明化合物的静电性质和拓扑结构显着影响PFAS的脱氟能力。对于新兴的PFAS，最佳模型表明，大多数化合物，例如全氟辛烷磺酸的潜在替代品，都难以进行还原脱氟，而全氟烷基醚羧酸比全氟辛酸具有相对更强的脱氟能力。理论计算表明，PFAS上的额外电子可能会导致分子解构，例如碳链二面角的变化，以及C-F键和醚C-O键的裂解。一般来说，当前的计算模型可用于筛选新兴的 PFAS，以评估其对含氟化合物结构分子设计的脱氟能力。
UV-generated hydrated electrons play a critical role in the defluorination reaction of poly- and perfluoroalkyl substances (PFAS). However, limited experimental data hinder insight into the effects of the structural characteristics of emerging PFAS on their defluorination abilities. Therefore, in this study, we adopted quantity structure−activity relationship models based on machine learning algorithms to develop the predictive models of the relative defluorination ability of PFAS. Five-fold cross-validations were used to perform the hyperparameter tuning of the models, which suggested that the gradient boosting algorithms with PaDEL descriptors as the best model possessed superior predictive performance (R2test= 0.944 and RMSEtest= 0.114). The importance of the descriptor indicated that the electrostatic properties and topological structure of the compounds significantly affected the defluorination ability of the PFAS. For the emerging PFAS the best model showed that most compounds, such as potential alternatives of perfluorooctane sulfonic acid, were recalcitrant to reductive defluorination, whereas perfluoroalkyl ether carboxylic acids had relatively stronger defluorination abilities than perfluorooctanoic acid. The theoretical calculations implied that additional electrons on PFAS could cause molecular deconstruction, such as changes in the dihedral angle involved in the carbon chain, as well as C–F bond and ether C–O bond cleavages. In general, the current computational models could be useful for screening emerging PFAS to assess their defluorination ability for the molecular design of fluorochemical structures.