New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts



Despite decades of effort, no earth abundant homogeneous catalysts have been discovered that can selectively oxidize methane to methanol. We exploit active learning to simultaneously optimize methane activation and methanol release calculated with machine learning (ML)-accelerated density functional theory (DFT) in a space of 16M candidate catalysts with novel macrocycles. By constructing these macrocycles from fragments inspired by synthesized compounds, we ensure synthetic realism in our computational search. Our large-scale search reveals that low spin Fe(II) compounds paired with strong field (e.g. P or S-coordinating) ligands have the best energetic tradeoff between hydrogen atom transfer (HAT) and methanol release. This observation is distinct from prior efforts that have focused on high spin Fe(II) with weak field ligands. By decoupling equatorial and axial ligand effects, we determine that negatively charged axial ligands are critical for more rapid release of methanol and, higher valency metals (i.e., M(III) vs M(II)) are unlikely to be suitable for methanol release. With full characterization of barrier heights, we confirmed that optimizing for HAT did not lead to large oxo formation barriers. Energetic span analysis revealed designs for an intermediate spin Mn(II) catalyst and a low spin Fe(II) catalyst that would lead to good turnover frequencies. This active learning approach is expected to be beneficial for search of large catalyst spaces where no prior designs have been identified and where linear scaling relationships between reaction energies or barriers may be limited or unknown.


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