Building a Toolbox for the Analysis and Prediction of Ligand and Catalyst Effects in Organometallic Catalysis

Abstract

Computers have become closely involved with most aspects of modern life and these developments are tracked in the chemical sciences. Recent years have seen the integration of computing across chemical research, made possible by investment in equipment, software development, improved networking between researchers and rapid growth in the application of predictive approaches to chemistry, but also a change of attitude rooted in the successes of computational chemistry -it is now entirely possible to complete research projects where computation and synthesis are cooperative, integrated, and work in synergy to achieve better insights and so improved results. It remains our ambition to put computational prediction before experiment, and we have been working towards developing the key ingredients and workflows to achieve this.The ability to precisely tune selectivity along with high catalyst activity make organometallic catalysts using transition metal (TM) centres ideal for high value-added transformations, and this can make them appealing for industrial applications. However, mechanistic variations of TM-catalysed reactions across the vast chemical space of different catalysts and substrates are not fully explored, nor is such an exploration feasible with current resources. This can lead to complete synthetic failures when new substrates are used, but more commonly we see outcomes that require further optimisation, such as incomplete conversion, insufficient selectivity, or the appearance of unwanted side products. These processes consume time and resources, but the insights and data generated are usually not tied to a broader predictive workflow where experiments test hypotheses quantitatively, reducing their impact.These failures suggest at least a partial deviation of the reaction pathway from that hypothesised, hinting at quite complex mechanistic manifolds for organometallic catalysts which are affected by the combination of input variables. Mechanistic deviation is most likely when challenging, multifunctional substrates are being used, and the quest for so-called privileged catalysts is quickly replaced by a need to screen catalysts libraries until a new "best" match between catalyst and substrate can be identified and reaction conditions optimised. As a community we remain confined to broad interpretations of the substrate scope of new catalysts and focus on small changes based on idealised catalytic cycles, rather than working towards a "big data" view of organometallic homogeneous catalysis, with routine use of predictive models and transparent data sharing.Databases of DFT-calculated steric and electronic descriptors can be built for such catalysts, and we summarise here how these can be used in the mapping, interpretation, and prediction of catalyst properties and reactivities. Our motivation is to make these databases useful as tools for synthetic chemists, so they challenge and validate quantitative computational approaches. In this account we demonstrate their application to different aspects of cataly...

Melding of Experiment and Theory Illuminates Mechanisms of Metal-Catalyzed Rearrangements: Computational Approaches and Caveats

“…For example, some are utilizing statistical tools to generate catalyst maps for dirhodium(II) 154 (and other) complexes to aid catalyst selection/ design. 188,189 Others are harnessing the power of machine learning methods for accelerated reaction discovery and chemical space exploration. [190][191][192] Nonetheless, we hope that we have given readers a snapshot of the utility of computational approaches through tales of transition-metalcatalyzed sigmatropic rearrangements.…”

Computational Organometallic Catalysis: Where We Are, Where We Are Going

“…A number of very recent reviews prove the fact that they are called to change the way in which catalysts are discovered. [51][52][53][54][55] The starting point of data-driven tools is establishing a relationship between a quantitative description of reactants, catalysts and reaction conditions with a property (for instance, activity: Quantitative Structure-Activity relationship, QSAR). The key ingredients of these models are the quantities (descriptors) that are correlated with the properties of interest.…”

SEQCROW: AChimeraXbundle to facilitate quantum chemical applications to complex molecular systems

“…Vital to many modern quantum chemistry applications is the evaluation of molecular descriptors. [22][23][24][25] SEQCROW provides an interface to compute and visualize Sterimol parameters, 26 both Tolman and "Exact" ligand cone angles, 27,28 and percent buried volume (%V bur ). [29][30][31][32] The former provides a multidimensional measure of the steric bulk of substituents, whereas the latter two quantify the steric crowding around a metal or other reactive center.…”

“…Vital to many modern quantum chemistry applications is the evaluation of molecular descriptors 22–25 . SEQCROW provides an interface to compute and visualize Sterimol parameters, 26 both Tolman and “Exact” ligand cone angles, 27,28 and percent buried volume (%V bur ) 29–32 .…”