Conspectus Catalyst design in enantioselective catalysis has historically been driven by empiricism. In this endeavor, experimentalists attempt to qualitatively identify trends in structure that lead to a desired catalyst function. In this body of work, we lay the groundwork for an improved, alternative workflow that uses quantitative methods to inform decision making at every step of the process. At the outset, we define a library of synthetically accessible permutations of a catalyst scaffold with the philosophy that the library contains every potential catalyst we are willing to make. To represent these chiral molecules, we have developed general 3D representations, which can be calculated for tens of thousands of structures. This defines the total chemical space of a given catalyst scaffold; it is constructed on the basis of catalyst structure only without regard to a specific reaction or mechanism. As such, any algorithmic subset selection method, which is unsupervised (i.e., only considers catalyst structure), should provide an ideal initial screening set for any new reaction that can be catalyzed by that scaffold. Notably, because this design strategy, the same set of catalysts can be used for any reaction that can be catalyzed with that parent catalyst scaffold. These are tested experimentally, and statistical learning tools can be used to create a model relating catalyst structure to catalyst function. Further, this model can be used to predict the performance of each catalyst candidate in the greater database of virtual catalyst candidates. In this way, it is possible estimate the performance of tens of thousands of catalysts by experimentally testing a smaller subset. Using error assessment metrics, it is possible to understand the confidence in new predictions. An experimentalist using this tool can balance the predicted results (reward) with the prediction confidence (risk) when deciding which catalysts to synthesize next in an optimization campaign. These catalysts are synthesized and tested experimentally. At this stage, either the optimization is a success or the predicted values were incorrect and further optimization is required. In the case of the latter, the information can be fed back into the statistical learning model to refine the model, and this iterative process can be used to determine the optimal catalyst. In this body of work, we not only establish this workflow but quantitatively establish how best to execute each step. Herein, we evaluate several 3D molecular representations to determine how best to represent molecules. Several selection protocols are examined to best decide which set of molecules can be used to represent the library of interest. In addition, the number of reactions needed to make accurate, statistical learning models is evaluated. Taken together these components establish a tool ready to progress from the development stage to the utility stage. As such, current research endeavors focus on applying these tools to optimize new reactions.

Although organophosphine syntheses have been known for the better part of a century, the synthesis of phosphines still represents an arduous task for even veteran synthetic chemists. Phosphines as a class of compounds vary greatly in their air sensitivity, and the misconception that it is trivial or even easy for a novice chemist to attempt a seemingly straightforward synthesis can have disastrous results. To simplify the task, we have previously developed a methodology that uses benchtop intermediates to access a wide variety of phosphine oxides (an immediate precursor to phosphines). This synthetic approach saves the air-free handling until the last step (reduction to and isolation of the phosphine). Presented herein is a complete general procedure for the facile reduction of phosphonates, phosphinates, and phosphine oxides to primary, secondary, and tertiary phosphines using aluminum hydride reducing agents. The electrophilic reducing agents ( i Bu)2AlH and AlH3 were determined to be vastly superior to LiAlH4 for reduction selectivity and reactivity. Notably, it was determined that AlH3 is capable of reducing the exceptionally resistant tricyclohexylphosphine oxide, even though LiAlH4 and ( i Bu)2AlH were not. Using this new procedure, gram-scale reactions to synthesize a representative range of primary, secondary, and tertiary phosphines (including volatile phosphines) were achieved reproducibly with excellent yields and unmatched purity without the need for a purification step.

This study reports the development and application of a conformer‐dependent quantitative quadrant descriptor for generating models that relate catalyst performance to catalyst structure in enantioselective transformations. The generality of these descriptors is demonstrated by using them for three different reactions: (1) copper‐catalyzed, enantioselective cyclopropanation of alkenes, (2) rhodium‐catalyzed enantioselective hydrogenation of α‐substituted N‐acyl‐enamides, and (3) enantioselective addition of thiols to N‐acyl imines. This work will provide researchers an interpretable steric descriptor that merges the heuristic value of quadrant models with a quantitative tool that can be used to create statistically meaningful correlations between the steric occupancy of catalyst quadrants and stereoselectivity. The low dimensionality of this descriptor, its ability to capture conformational effects and stereostructure, and its direct relationship to intuitive structural properties make it particularly well‐suited for creating Quantitative Structure‐Selectivity Relationships (QSSR) with smaller datasets of asymmetric reactions.

The synthesis of phosphine macrocycles is a relatively underdeveloped area and no standard synthetic routes have emerged. Accordingly, two general synthetic routes to tetradentate phosphine macrocycles were investigated. Both routes use Cu(i) ions as template ions because, unlike other metals such as Pd(ii) and Pt(ii), the Cu(i) ions can be removed from the macrocyclic complex without degrading the macrocycle ligand. The first route involves the coupling of two bidentate secondary phosphines bonded to Cu(i) using 1,3-dibromopropane or 1,4-dibromobutane. Using this route, tetradentate phosphine macrocycles with either -(CH2)3OCH3 or Ph groups bonded to the P atoms were synthesized. Macrocycle phosphines containing the -(CH2)3OCH3 groups were investigated for their potential water-solubility, but experiments showed these phosphines were not water soluble. The second synthetic route involved the alkylation of an open-chain, mixed tertiary-secondary, tetradentate phosphine coordinated to Cu(i). Following formation of the macrocyclic ligand, the Cu(i) template was removed by reaction with aqueous KCN to yield the free macrocyclic phosphine. This route was demonstrated for the preparation of the macrocyclic phosphine ligand 1,5,9,13-tetraphenyl-1,5,9,13-tetraphosphacycloheptadecane. Following demetallation, this macrocyclic ligand was coordinated to Fe(ii) and Co(ii) to form the corresponding macrocyclic phosphine complexes.