Machine learning for predicting halogen radical reactivity toward aqueous organic chemicals.

Rapid advances in machine learning (ML) provide fast, accurate, and widely applicable methods for predicting free radical-mediated organic pollutant reactivity. In this study, the rate constants (logk) of four halogen radicals were predicted using Morgan fingerprint (MF) and Mordred descriptor (MD) in combination with a series of ML models. The findings highlighted that making accurate predictions for various datasets depended on an effective combination of descriptors and algorithms.

The application of machine learning methods for prediction of heavy metal by activated carbons, biochars, and carbon nanotubes.

Carbonaceous materials are commonly used as adsorbents for heavy metals. The determination of the adsorption capacity needs time and energy, and the key factors affecting the adsorption capacity have not been determined. Therefore, a new and efficient method is needed to predict the adsorption capacity and explore the decisive factors in the adsorption process.

Prediction of antibiotic sorption in soil with machine learning and analysis of global antibiotic resistance risk.

Although the sorption of antibiotics in soil has been extensively studied, their spatial distribution patterns and sorption mechanisms still need to be clarified, which hinders the assessment of antibiotic resistance risk. In this study, machine learning was employed to develop the models for predicting the soil sorption behavior of three classes of antibiotics (sulfonamides, tetracyclines, and fluoroquinolones) in 255 soils with 2203 data points. The optimal independent models obtained an accurate predictive performance with R of 0.