Enhancing arsenate removal through interpretable machine learning guiding the modular design of metal–organic frameworks

Abstract

The modular design of metal–organic frameworks (MOFs) for enhancing arsenate (As(V)) adsorption remains a challenge. We have developed twelve interpretable machine learning prediction models by integrating six decision tree-based algorithms with two molecular fingerprints, Morgan and MACCS. The optimal XGBoost-Morgan model, with its high determination coefficient of 0.97 and low root mean square error of 11.78, not only demonstrates excellent prediction and generalization capabilities in As(V) adsorption capacity, but also validates the feasibility of modeling concepts by feature engineering adopted in this work. Our comprehensive model interpretation initiates with the molecular fragment composition of the organic linkers, the secondary building units, and the topological nets, that constitute MOFs, and structure parameters. It culminates in the proposal of a reticular chemistry design scheme specifically tailored for the adsorption of As(V) by MOFs. Furthermore, it delineates the optimal environmental conditions conducive to effective As(V) adsorption. We anticipate that our MOF design strategy will be extensively applicable to the target removal of pollutants.