Linking Mechanistic Analysis of Catalytic Reactivity Cliffs to Ligand Classification

Abstract

Statistical analysis of reaction data with molecular descriptors can enable chemists to identify reactivity cliffs that result from a mechanistic dependence on a specific structural feature. In this study, we develop a broadly applicable and quantitative classification workflow that identifies reactivity cliffs in eleven Ni- and Pd-catalyzed cross-coupling datasets employing monodentate phosphine ligands. A unique ligand steric descriptor, %<i>V</i><sub>bur</sub> (<i>min</i>), is found to divide these datasets into active and inactive regions at a similar threshold value. Organometallic studies demonstrate that this threshold corresponds to the binary outcome of bisligated versus monoligated metal and that %<i>V</i><sub>bur</sub> (<i>min</i>) is a physically meaningful and predictive representation of ligand structure in catalysis. Taken together, we expect that this strategy will be of broad value in mechanistic investigation of structure-reactivity relationships, while providing a means to rationally partition datasets for data-driven modeling.

A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis

“…The database and tools reported herein are currently being applied to enhance reaction optimization 47 and mechanistic workflows. 48 The open-source nature of our codes, as well as the open database, is designed to be extended by others and we welcome further contributions by the community.…”

Development and Molecular Understanding of a Pd‐Catalyzed Cyanation of Aryl Boronic Acids Enabled by High‐Throughput Experimentation and Data Analysis

“…Previous studies by the Doyle and Sigman groups have demonstrated that threshold analyses could successfully be used to classify ligands according to their reactivity for several Ni and Pd-catalyzed cross-coupling reactions. [81] A sharp reactivity cliff could be found with the largest PÀ C antibonding energy of the ligand (Eσ* (PÀ C)max L ) at a threshold value of 0.237 Hartree, allowing us to bin the outcomes accurately (see Figure 4,b): all ligands above 0.237 were classified as active (white area), whereas the ones with values below were classified as inactive (gray area). The cutoff for inactive entries has been set, as mentioned before, to 5 % yield (less than one turnover), hence dividing the plot in areas with true negatives (below 0.237, below 5 %) and false negatives (below 0.237, above 5 %).…”

Genetic Optimization of Homogeneous Catalysts

“…Furthermore, evolutionary experiments with GAs lead to alternative chemical insight into catalyst performance, as demonstrated hereafter. GAs have been shown to be well-suited for molecular optimization, [9,18,19] because they are able to address discontinuities in structure-property space (e. g., activity cliffs) [20,21] and, more importantly, do not require meaningful gradients for the optimization. Nonetheless, flexible and robust implementations of GA algorithms tailored for homogeneous catalysis were lacking.…”