Leveraging Quantum Chemistry and Machine Learning for the Design of Low-Valent Transition Metal Catalysts in Nitrogen to Ammonia Conversion

The conversion of N₂ to NH₃ under ambient conditions is a major goal in sustainable chemistry. Homogeneous catalysts, particularly those employing cyclic(alkyl)(amino)carbene (CAAC) ligands, have demonstrated promise in stabilizing low-valent Fe centers, yet industrial-level turnover numbers (TONs) and frequencies (TOFs) remain unmet. Here, we integrate quantum chemistry, molecular dynamics, and machine learning (ML) to uncover mechanistic features governing nitrogen reduction reaction (NRR) activity and guide catalyst design. Density functional theory (DFT) and ab initio molecular dynamics reveal that [Fe(CAAC)₂] leverages redox noninnocent CAAC ligands to stabilize Fe(I) ([Fe^I(CAAC)₂^·-]), with strong antiferromagnetic coupling (J_Fe-CAAC = -1817 cm^-1). Flexibility of bulky Dipp groups found to hinder N₂ binding, rationalizing experimental observations. The exothermic formation of [(CAAC(H))₂Fe] (ΔG = -4.5 kJ/mol) with in situ generated H₂ exposure rationalizes the lower TON observed via catalyst deactivation. ML models trained on quantum descriptors such as M-C bond lengths, spin density, and frontier orbital energies identify the M-C distance as a key predictor of reactivity. A composite free energy metric (ΔG_tot) encompassing cis-trans isomerization (ΔG₁⁰), N₂ binding (ΔG₂⁰), and the first reduction step (ΔG₃⁰) enables ranking of candidate catalysts. Moreover, Ti and V complexes show the lowest ΔG_tot (24-60 kJ/mol), while late transition and coinage metals exceed 120 kJ/mol, correlating with lower activity. By providing unprecedented insights into the interplay among ligand design, metal choice, and catalytic efficiency, this work lays a critical foundation for the rational design of homogeneous NRR catalysts, with implications for advancing sustainable ammonia production technologies.