Genetic Algorithms for the Discovery of Homogeneous Catalysts

In this account, we discuss the use of genetic algorithms in the inverse design process of homogeneous catalysts for chemical transformations. We describe the main components of evolutionary experiments, specifically the nature of the fitness function to optimize, the library of molecular fragments from which potential catalysts are assembled, and the settings of the genetic algorithm itself. While not exhaustive, this review summarizes the key challenges and characteristics of our own (i.e., NaviCatGA) and other GAs for the discovery of new catalysts

Understanding catalyst structure-selectivity relationships in Pd-catalyzed enantioselective methoxycarbonylation of styrene

The EPSRC are thanked for funding PD and JAF in the very early stages of this work (EP/M003868/1).Catalyst-controlled regioselectivity in palladium-catalyzed carbonylation of alkenes has been a long-standing goal of homogeneous catalysis. In general, monophosphines do favor branched regioselectivity, but lead to poor enantioselectivity, while diphosphines give mainly linear products. Previously, we reported the simultaneous control of regio- and enantioselectivity in the hydroxy- and methoxycarbonylation of vinyl arenes with Pd complexes of the Phanephos ligand. Herein, we present a density functional theory study (B3PW91-D3 level of theory) of the catalytic cycle, supported by deuterium labeling studies, to understand its mechanism. Alkene coordination to a Pd-hydride species was identified as the origin of asymmetric induction and regioselectivity in both the parent Pd/Xylyl-Phanephos catalyst and electron-deficient analogue, and rationalized according to a quadrant-diagram representation. The mechanism by which the preferentially formed pro-(S) Pd-alkene complex can isomerize via rotation around the palladium–(C═C) bond was investigated. In the parent system, this process is in competition with the methanolysis step that leads to the ester product and is responsible for the overall loss of regioselectivity. On the other hand, the introduction of electron-withdrawing substituents on the catalyst framework results in the reduction of the methanolysis barriers, making the isomerization pathway energetically unfavorable and so leading simultaneously to high regiocontrol and good enantiomeric ratios.PostprintPeer reviewe

OSCAR: An Extensive Repository of Chemically and Functionally Diverse Organocatalysts

The automated construction of datasets has become increasingly relevant in computational chemistry. While transition-metal catalysis has greatly benefitted from bottom-up or top-down strategies for the curation of organometallic complexes libraries, the field of organocatalysis is mostly dominated by case-by-case studies, with a lack of transferable data-driven tools that facilitate both the exploration of a wider range of catalyst space and the optimization of reaction properties. For these reasons, we introduce OSCAR, a repository of thousands of experimentally derived or combinatorially enriched organocatalysts and their corresponding building blocks. We outline the fragment-based approach used for database generation and showcase the chemical diversity, in terms of functions and molecular properties, covered in OSCAR. The structures and corresponding stereoelectronic properties are publicly available and constitute the starting point to build generative and predictive models for organocatalyst performance

Genetic Algorithms for the Discovery of Homogeneous Catalysts

In this account, we discuss the use of genetic algorithms in the inverse design process of homogeneous catalysts for chemical transformations. We describe the main components of evolutionary experiments, specifically the nature of the fitness function to optimize, the library of molecular fragments from which potential catalysts are assembled, and the settings of the genetic algorithm itself. While not exhaustive, this review summarizes the key challenges and characteristics of our own (i.e., NaviCatGA) and other GAs for the discovery of new catalysts

Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts

Hundreds of catalytic methods are developed each year to meet the demand for high-purity chiral compounds. The computational design of enantioselective organocatalysts remains a significant challenge, as catalysts are typically discovered through experimental screening. Recent advances in combining quantum chemical computations and machine learning (ML) hold great potential to propel the next leap forward in asymmetric catalysis. Within the context of quantum chemical machine learning (QML, or atomistic ML), the ML representations used to encode the three-dimensional structure of molecules and evaluate their similarity cannot easily capture the subtle energy differences that govern enantioselectivity. Here, we present a general strategy for improving molecular representations within an atomistic machine learning model to predict the DFT-computed enantiomeric excess of asymmetric propargylation organocatalysts solely from the structure of catalytic cycle intermediates. Mean absolute errors as low as 0.25 kcal mol(-1) were achieved in predictions of the activation energy with respect to DFT computations. By virtue of its design, this strategy is generalisable to other ML models, to experimental data and to any catalytic asymmetric reaction, enabling the rapid screening of structurally diverse organocatalysts from available structural information

Understanding catalyst structure-selectivity relationships in Pd-catalyzed enantioselective methoxycarbonylation of styrene (dataset)

Data underpinning publicatio

Understanding catalyst structure-selectivity relationships in Pd-catalyzed enantioselective methoxycarbonylation of styrene (dataset)

Data underpinning publicatio

Computational and theoretical approaches for mechanistic understanding: general discussion

status: publishe

Computational and theoretical approaches for mechanistic understanding: general discussion

International audienc

Mechanistic insight into organic and industrial transformations: general discussion

status: publishe