Concentration division for adsorption coefficient prediction using machine learning with Abraham descriptors: Data-splitting approach comparison and critical factors identification

Machine learning (ML) including Abraham descriptors from polyparameter linear free energy relationships (pp-LFERs) has been a popular method for the adsorption coefficient (K_d) prediction. However, Abraham descriptors from pp-LFERs are concentration-dependent and the significance of these descriptors can change over different adsorbate concentrations. Ignoring concentration effects on the adsorption process and K_d prediction will hinder the understanding of interactions among solutes, solvents and adsorbents at different equilibrium concentration (C_e) ranges. Therefore, our study first systematically investigated the concentration effects on micropollutant adsorption to carbon-based adsorbents using ML with Abraham descriptors. Concentration-selection approach, as a new data-splitting approach, divided the whole dataset according to the different C_e ranges. This concentration-selection approach performed better than the data-splitting approach used in previous studies. After the ML models were built in different C_e subsets, Shapley values were calculated to quantify input descriptor contributions. The results indicated specific surface area (BET) was the only critical factor when C_e was in the highest range. The importance of Abraham descriptors increased gradually when C_e decreased. Total pore volume (V_t) was a far less important feature than BET for K_d prediction. Critical factors identified at different C_e ranges for K_d prediction provide a guidance for novel carbon-based adsorbent design.