Multiobjective Optimization of Synthetically Accessible Homogeneous Catalysts Reveals Tradeoffs for Methane-to-Methanol Conversion

The selective conversion of methane to methanol remains a major challenge in catalysis, and no earth-abundant homogeneous catalyst has been demonstrated to carry out this transformation with high turnover. To explore previously synthesized complexes that might be amenable for this transformation, we start from macrocyclic ligands from experimental structures in the Cambridge Structural Database. After expanding this set of ligands with Hammett-tuning functionalization, we construct a design space of 21.9 million Mn and Fe catalysts. We employ active learning using neural network surrogate models with quantified uncertainty to efficiently identify catalysts with optimal methane-to-methanol turnover. We do this by jointly optimizing density functional theory (DFT)-computed hydrogen atom transfer (HAT) and methyl radical rebound energetics. Square planar tetraaza-based macrocycles in combination with low-spin Fe(II) or Mn(II) emerge as a privileged class of transition metal complexes within the Pareto-optimal set, with Mn(II) catalysts exhibiting balanced energetic tradeoffs of the two steps. In contrast, porphyrin catalysts exhibit favorable HAT but unfavorable rebound, and seesaw catalysts, which are not widely employed, are indeed the least favorable in our set. Full reaction coordinate analysis of representative Pareto-optimal catalysts reveals that HAT and rebound thermodynamics no longer govern turnover, which is instead determined by the stability of the metal–oxo intermediate and the rebound kinetic barrier. Nonetheless, the resulting energetic spans for these catalysts remain competitive with known earth-abundant, methane-to-methanol catalysts, underscoring the effectiveness of active learning for discovering previously unexplored, synthetically accessible catalysts and unraveling catalyst design principles for methane-to-methanol reactivity.