Unified electronic-geometric descriptor deciphers peroxymonosulfate activation using Fe-based dual-atom catalysts

The rational design of high-efficiency Fenton-like catalysts remains hindered by insufficient understanding of electronic-geometric synergy in peroxymonosulfate (PMS) activation. We transcend classical d-band theory by proposing a machine learning-decoded binary descriptor (BD) unifying orbital electronic structure (I_OES) and orbital geometric structure (I_OGS) indices to predict PMS activation pathways across diverse coordination environments. This BD framework quantifies antibonding orbital occupancy (via d-p hybridization) and geometric constraints (interatomic distance/O-H elongation), enabling precise screening of Fe-based dual-atom catalysts (DACs). Among FeM DACs (M = Ti, V, Cr, Mn, Fe, Co, Ni, Cu), FeMn DACs with optimal BD values (I_OES = 0.86, I_OGS = 0.40) achieved 94.2% ¹O₂ yield and fast kinetics (k_obs = 1.2 min⁻¹) toward sulfadiazine degradation. Crucially, a flow-through reactor demonstrated >90% pollutant removal for 30 days at industrial flux (122.3 L m⁻² h⁻¹). This work establishes universal orbital-level design principles for sustainable water remediation, bridging atomic-scale insights to engineering-scale implementation.