Researchers have developed a deep learning model that predicts how fast biochar materials can break down antibiotic pollutants in water. The model, tested on data from 75 studies, identifies the key traits of biochar and the reaction conditions that drive degradation. This could help researchers screen and design better biochar catalysts without running as many expensive experiments. The study found that catalyst properties like persistent free radicals and pore volume accounted for nearly 60% of the model's predictive power. The AI tool is available through a web interface and can estimate degradation rates with errors under 20%. It offers a practical way to move from trial and error toward data guided design for water treatment systems.
