Inverse design of frustrated Lewis pairs for direct catalytic CO2 hydrogenation: refining and expanding design rules

TL;DR

This paper uses genetic algorithms to optimize FLP catalysts for CO2 hydrogenation, refining design rules and discovering new active motifs.

Contribution

A genetic algorithm workflow is introduced to refine and expand FLP design principles for CO2 hydrogenation.

Findings

Top FLP candidates for CO2 hydrogenation were identified using a genetic algorithm and activity maps.

New active FLP motifs were discovered that were not previously reported.

Design principles for FLPs were refined and extended based on the analysis of Pareto fronts.

Abstract

Frustrated Lewis pairs (FLPs), composed of reactive combinations of Lewis acids (LAs) and bases (LBs) offer a metal-free platform for catalyzing a wide range of chemical transformations. Designing the optimal FLP active site for a particular chemical reaction is a challenging task due to the lack of rigorous principles and countless chemical possibilities. We recently designed principles, which outline the relative disposition (i.e., distance and angle) and chemical composition of the LA and LB centers that maximize activity in B- and N-based FLPs. These criteria were already used to screen 25 000 FLP active sites built on N-containing linkers extracted from the CoRE MOF dataset, but in such an enormous multifunctional catalyst space, inverse design approaches provide a more efficient mean to explore all possible combinations. Here, we use the NaviCatGA genetic algorithm to simultaneously optimize the chemical and geometrical characteristics of intramolecular FLPs while considering synthetic complexity and catalyst quenching constraints. By integrating activity maps and non-linear regression models, our workflow explores a vast chemical space of 1.7 billion FLP candidates built from organic fragments curated from the literature—released as the open-source FragFLP25 dataset—to identify optimal compositions suitable for catalytic CO2 hydrogenation. Analyzing the top candidates extracted from various Pareto fronts in the catalyst space, we not only uncover active FLP motifs for hydrogenation that have not been previously reported but also refine and extend the design principles previously established from our high-throughput screening study.

Genetic optimization of frustrated Lewis pair catalysts refines design rules and reveals unexplored motifs for CO2 hydrogenation.

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