Dual regulatory mechanisms and machine learning analysis of nitrate reduction: a triatomic heteronuclear catalyst on...

This study systematically explores homonuclear/heteronuclear trimetallic catalysts (TM₃@g-C₂N) for sustainable NH₃ synthesis by integrating density functional theory (DFT) and machine learning (ML) analyses. The Fe₂Mn@g-C₂N heterostructure demonstrates exceptional performance with a record-low limiting potential of -0.25 V, outperforming homonuclear counterparts (Fe₃@g-C₂N: -0.30 V) and benchmark single-atom catalysts (Hf@g-C₂N: -0.27 V). Mechanism exploration reveals dual-regulation mechanisms: (1) Geometric strain from Fe/Mn atomic difference (ΔR = 0.12 Å) creates asymmetric active pockets that steer NO intermediates toward the end-on adsorption configuration, and (2) Electron complementarity drives interfacial charge transfer (Fe → Mn: 0.23 e^-) to optimize d-band center. Through a robust ML framework combining LASSO, SISSO, GBR, and Bootstrap validation, we identified the intermetal distance (D_M1-M2) and the total electronegativity sum (E_sum) as the most critical activity descriptors. The derived quantitative model (SISSO, GBR) confirms a strong nonlinear coupling between geometric and electronic properties. This work establishes a "geometric-electronic compatibility" principle for trimetallic catalysts, offering a dual solution to environmental nitrate remediation and green ammonia synthesis.