Data-driven insights into nonradical activation mechanisms for biochar inverse design: A synergistic approach using DFT and machine learning with meta-analysis

Machine learning (ML) is recognized as a potent tool for the inverse design of environmental functional material, particularly for complex entities like biochar-based catalysts (BCs). Thus, the tailored BCs can have a distinct ability to trigger the nonradical pathway in advance oxidation processes (AOPs), promising a stable, rapid and selective degradation of persistent contaminants. However, due to the inherent “black box” nature and limitations of input features, results and conclusions derived from ML may not always be intuitively understood or comprehensively validated. To tackle this challenge, we linked the front-point interpretable analysis approaches with back-point density functional theory (DFT) calculations to form a chained learning strategy for deeper sight into the intrinsic activation mechanism of BCs in AOPs. At the front point, we conducted an easy-to-interpret meta-analysis to validate two strategies for enhancing nonradical pathways by increasing oxygen content and specific surface area (SSA), and prepared oxidized biochar (OBC500) and SSA-increased biochar (SBC900) by controlling pyrolysis conditions and modification methods. Subsequently, experimental results showed that OBC500 and SBC900 had distinct dominant degradation pathways for ¹O₂ generation and electron transfer, respectively. Finally, at the end point, DFT calculations revealed their active sites and degradation mechanisms. This chained learning strategy elucidates fundamental principles for BC inverse design and showcases the exceptional capacity to integrate computational techniques to accelerate catalyst inverse design.