(477b) Finding Needles in a Haystack: Sifting through 16M Catalysts for Optimal Methane-to-Methanol Catalyst Design Under Weak Thermodynamic Scaling

The selective partial oxidation of methane-to-methanol has been a Holy Grail challenge for well-over half of a century. Computational high-throughput virtual screening (HTVS) with first-principles density functional theory (DFT) can play a valuable role in unearthing design rules for scalable and viable synthetic analogues that preserve selectivity and activity observed only in enzymes. A number of enzymes (e.g., methane monooxygenase) display activity for functionalization of inert C-H bonds. Single-site catalysts represent the most promising synthetic analogues to these enzymes, often enabling atom-economy, tunability, and selectivity not possible with bulk heterogeneous catalysts.

Due to strong oxidation and spin-state dependence on the relative energetics of reactive intermediates on the methane-to-methanol energy landscape, linear free energy relationships that are invoked during HTVS to simplify catalyst screening cannot be readily used. As an alternative approach, the absence of universal scaling relations between intermediate energetics provides an opportunity for non-linear machine learning (ML) models that can be used over a larger space of candidate materials. Rather than relying on linear relationships between quantities, ML models can be trained to directly predict catalyst reactivity on the basis of chemical composition and applied to thousands of compounds.

We sift through a large space of possible compounds to evaluate candidate catalysts. Here, we draw inspiration from experimentally synthesized macrocycles (e.g. porphyrins, cyclams, corroles, and phthalocyanines), and recombine their pieces to make new macrocycles. We use ML model driven efficient global optimization with a 2D expected improvement criterion to simultaneously optimize the thermodynamics for C-H bond activation (e.g. hydrogen atom transfer), and methanol release energetics. This creates a Pareto front of methane oxidation catalysts that demonstrate the best tradeoffs between C-H bond activation and methanol release. We use active learning to improve model performance on the Pareto front and generate novel lead candidate materials that we validate by DFT.