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 and integrated, and work in synergy to achieve better insights and improved results. It remains our ambition to put computational prediction before experiment, and we have been working toward 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) centers ideal for high-value-added transformations, and this can make them appealing for industrial applications. However, mechanistic variations of TM-catalyzed reactions across the vast chemical space of different catalysts and substrates are not fully explored, and such an exploration is not 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 optimization, 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 hypothesized, hinting at quite complex mechanistic manifolds for organometallic catalysts that 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 catalyst libraries until a new "best" match between the catalyst and substrate can be identified and the reaction conditions can be optimized. As a community we remain confined to broad interpretations of the substrate scope of new catalysts and focus on small changes based on idealized catalytic cycles rather than working toward 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 summarize 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 that they challenge and validate quantitative computational approaches. In this Account, we demonstrate their application to different aspects of catalyst design and discovery and their integration with computational mechanistic studies and thus describe the progress of our journey toward truly predictive models in homogeneous organometallic catalysis.
Highlights
These new ligands proved to be promising for catalysis, achieving activities and selectivity comparable to ligands used in industry for both the hydroformylation of 1-heptene (PPh3) and the hydrocyanation of 3-pentenenitrile (P(O-o-Tol)[3]). This observation corresponded well with their location on the map, and we further demonstrated that proximity in ligand space can correspond to similar chemical behaviour[35] and that projection of experimental observations onto the map can highlight clusters of ligands that give rise to active catalysts.[29,36]. These applications of Ligand Knowledge Base (LKB)-P data are most useful for the design and analysis of experimental screening campaigns, but hold limited predictive power alone
We have demonstrated the use of ligand knowledge base for monodentate phosphines (LKB-P) descriptors for the analysis of ligand effects on calculated barriers.[30]
Her group use computational approaches as a driver for scientific discovery
Summary
While computational results are routinely used to contribute to the analysis of experimental data and confirmation of mechanistic postulates, computational predictions made before experiments remain the exception[4] in organometallic catalysis. Other groups utilising different approaches have published successful examples, predicting the outcome of reactions such as Buchwald-Hartwig cross-coupling, nucleophilic aromatic substitution, and alkene ethenolysis.[13-15] These quantitative models, which combine computed descriptors with experimental data and statistical techniques such as multivariate linear regression (MLR) and machine learning algorithms, have been shown to be successful in predicting the outcomes of reactions within a well-defined region of catalyst space. Our own efforts have approached the problem of prediction by providing chemists with tools designed to be widely applicable to organometallic chemistry, as well as responsive to subtle changes in catalyst properties, providing a platform for large-scale mapping and predictive modelling in this field This is a shared endeavour which has databases of calculated catalyst descriptors developed in Bristol at its heart. It is worth bearing in mind that “prediction” can range from a data-led design of experiments to a structure-based computational guide to the “best” catalyst for a given substrate; the former can be achieved but the latter remains a formidable challenge, since few catalytic routes are mechanistically robust, nor are they fully mapped
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