Integrated machine learning and density functional theory to design C₃N₅ piezo-catalysts toward precise control of...

The precise modulation of reactive oxygen species (ROS) generation pathways is crucial for enhancing the selectivity and efficiency of piezo-catalytic water purification. Herein, we report a mechanism-informed machine learning (ML) strategy integrating density functional theory (DFT) to rationally design non-metal-doped C₃N₅ piezo-catalysts. Using dipole moment as a key descriptor, our optimized ML model achieved high predictive accuracy and identified boron (B), sulfur (S), and phosphorus (P) as optimal dopants. Experimental validation confirmed that S-doping significantly enhanced polarization and charge separation, leading to generation of •OH and •O₂^- and superior degradation performance of sulfamethoxazole (SMX). In contrast, B-doping favored a hole-driven •OH pathway. Crucially, S-doped catalyst maintained high efficiency and stability across a wide pH range and in realistic water matrices. This work demonstrates the power of ML-guided design for developing advanced piezo-catalysts with tailored ROS pathways for efficient environmental remediation.