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-accelerated
density functional theory in a space of 16 M candidate catalysts including
novel macrocycles. By constructing 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
among the best energetic tradeoffs between hydrogen atom transfer
(HAT) and methanol release. This observation contrasts with 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 that higher-valency metals [i.e., M­(III) vs M­(II)]
are likely to be rate-limited by slow methanol release. With full
characterization of barrier heights, we confirm that optimizing for
HAT does not lead to large oxo formation barriers. Energetic span
analysis reveals designs for an intermediate-spin Mn­(II) catalyst
and a low-spin Fe­(II) catalyst that are predicted to have good turnover
frequencies. Our active learning approach to optimize two distinct
reaction energies with efficient global optimization is expected to
be beneficial for the 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.