期刊
SCIENCE OF THE TOTAL ENVIRONMENT
卷 859, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.scitotenv.2022.160228
关键词
Water treatment; Organic contaminants; Artificial intelligence; Adsorption isotherm
Adsorption of organic molecules can be effectively predicted using machine learning methods based on chemical structure, which can differentiate structural differences and provide relevant information for their interaction with adsorbents.
Adsorption of organic molecules from aqueous solution offers a simple and effective method for their removal. Re-cently, there have been several attempts to apply machine learning (ML) for this problem. To this end, polyparameter linear free energy relationships (pp-LFERs) were employed, and poor prediction results were observed outside model applicability domain of pp-LFERs. In this study, we improved the applicability of ML methods by adopting a chemical -structure (CS) based approach. We used the prediction of adsorption of organic molecules on carbon-based adsorbents as an example. Our results show that this approach can fully differentiate the structural differences between any or-ganic molecules, while providing significant information that is relevant to their interaction with the adsorbents. We compared two CS feature descriptors: 3D-coordination and simplified molecular-input line-entry system (SMILES). We then built CS-ML models based on neural networks (NN) and extreme gradient boosting (XGB). They all outperformed pp-LFERs based models and are capable to accurately predict adsorption isotherm of isomers with similar physiochemical properties such as chiral molecules, even though they are trained with achiral molecules and race -mates. We found for predicting adsorption isotherm, XGB shows better performance than NN, and 3D-coordinations allow effective differentiation between organic molecules.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据