Computational Mapping of Dirhodium(II) Catalysts

Abstract

The chemistry of dirhodium(II) catalysts is highly diverse, and can enable the synthesis of many different molecular classes. A tool to aid in catalyst selection, independent of mechanism and reactivity, would therefore be highly desirable. Here, we describe the development of a database for dirhodium(II) catalysts that is based on the principal component analysis of DFT‐calculated parameters capturing their steric and electronic properties. This database maps the relevant catalyst space, and may facilitate exploration of the reactivity landscape for any process catalysed by dirhodium(II) complexes. We have shown that one of the principal components of these catalysts correlates with the outcome (e.g. yield, selectivity) of a transformation used in a molecular discovery project. Furthermore, we envisage that this approach will assist the selection of more effective catalyst screening sets, and, hence, the data‐led optimisation of a wide range of rhodium‐catalysed transformations.

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

“…186,187 In this review, we have merely touched the tip of the iceberg when it comes to applied computational approaches for solving complex mechanistic problems. 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.…”

“…For instance, a substantial amount of mechanistic insight has been generated by studies involving close collaboration between groups specializing in theory and experiment: ranging from C-H [144][145][146][147] and Si-H insertion, 148,149 cyclopropanation, 145,[150][151][152][153] and mapping catalyst space. 154 In this section, we review representative mechanistic studies of metal-catalyzed (mainly Rh and Cu) sigmatropic rearrangement reactions that have benefitted from attention from both experiment and theory camps.…”

Mechanistic Inference from Statistical Models at Different Data-Size Regimes

“…Beyond trend evaluation, another powerful visualization technique for the identification of mechanism breaks are cutoff values in scatter plots. To streamline this approach, one can use computational ligand libraries that function as repositories of molecular parameters covering a broad chemical space. − Pioneering work by the Fey group established libraries mostly specializing in phosphine ligands, motivated by their prevalence in cross-coupling reactions. ,,− , More recently, a new library of monodentate phosphines, extended by ML techniques, was reported by the Aspuru-Guzik and Sigman groups . The library is incorporated in an online platform named Kraken, and 200 descriptors are available for each ligand in the database.…”

ChemSpaX: exploration of chemical space by automated functionalization of molecular scaffold

“…31 SPDBs with a dense representation of the chemical space of catalytic scaffolds can help discover design principles leading to the development of sustainable catalytic systems for various applications. 31,[39][40][41][42][43][44] Representations of the molecular structure is of high importance in SPDBs. Several approaches have been developed in this regard recently.…”