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

ABSTRACT: 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-

SUBMITTER: Nandy A 

PROVIDER: S-EPMC9135396 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature