Accelerating bioelectrodechlorination via data-driven inverse design.

Microbial electrorespiration harnesses bacteria to drive reductive dechlorination, offering a sustainable method to remediate environments contaminated with persistent chlorinated organic pollutants (COPs). However, aquifers' complex hydrogeological and hydrochemical conditions, combined with uneven COP distribution, create substantial spatial and temporal variability in biochemical reactions, environmental factors, and microbial communities. Traditional trial-and-error experiments are labor-intensive and slow, impeding the quick identification of conditions that accelerate dechlorination rates. Here we show that a machine learning framework, integrating experimental design with cathodic biofilm data, uncovers key interrelationships among environmental variables, dechlorination kinetics, electrochemical properties, and functional microbes, enabling rapid optimization of bioelectrodechlorination. Trained on literature-derived datasets using models such as extreme gradient boosting, random forest, and multilayer perceptron, this framework identifies temperature and cathode potential as primary drivers in experimental design while highlighting key biofilm genera, including