Executive Summary : | Remarkable progress in making chiral molecules using different strategies of asymmetric catalysis has already been reported, but acquiring precise and simultaneous control over both relative and absolute configurations of geminal stereogenic centers continues to present formidable challenges. In other words, accomplishing high diastereoselectivity without loss of enantioselectivity remains a difficult task in asymmetric synthesis of compounds with two or more contiguous stereogenic centers. A number of valuable strategies to tackle the issue of stereocontrol are now beginning to appear in literature. A stereodivergent process is the one through which we are able to generate any stereoisomer of the product with multiple stereocenters under same set of reaction conditions from the same set of starting substrates. This project tends to examine the detailed mechanism of substrate activation by two different chiral catalysts, the origin of enantio- and diastereoselectivities associated with the formation of product, and whether or not a cooperative mode of action exists between the two chiral catalysts. After gaining molecular insights on the origin of stereoinduction, in stereodivergent reactions through electronic structure theory calculation by means of density functional theory computation, the gained knowledge can be extended to rational design of a cooperative catalytic system (catalysts). The thermodynamics and kinetic parameters like Gibbs free energy and enthalpy of each stationary states such as prereacting complex, active catalytic species, transition states which includes stereodivergent transition states, activation barrier of transition states, role of solvent and additives or co-catalysts will be analyzed in order to decipher the origin selectivity and reactivity. Molecular insights on stereocontrol in transition states, gained using modern electronic structure computations, can lead to the development of faster reliable catalyst design techniques that can guide future experiments of high contemporary interest. Recent applications of ML in chemistry involves computer assisted synthetic planning, predicting catalytic properties and reactions outcomes, machine learned atomistic potentials and medicinal chemistry discovery, catalyst design, materials discovery, the enhancement of computer simulation techniques, the optimization of reaction conditions, which represent the beginning of a new era. These machine learning techniques have been used to study the quantitative structure-activity relationships thus enabling the rapid optimization of catalytic transformations. The molecular insights obtained through electronic structure calculations can be used as descriptors or as input information for the training of machine learning algorithms and the gained information can be used for the prediction of completely unknown or out of box reactions catalyzed by catalysts belonging to the same class or family of catalysts. |