Predictive Computational Modelling for the Rational Design of Divergent Nickel Catalysis

Objective

"DiNiCa (Divergent Nickel Catalysis) represents a major frontier in chemical synthesis, offering the potential to generate multiple, distinct products from identical starting materials simply by changing the ancillary ligand on a Nickel catalyst. While experimentally powerful, this approach is severely limited by a lack of fundamental mechanistic understanding, forcing catalyst development to rely on empirical, trial-and-error screening. The central knowledge gap is the unelucidated electronic role of the directing ligand, many of which are ""non-innocent"", in controlling reaction selectivity. The PCoM-RaDeDiNiCa (Predictive Computational Modelling for the Rational Design of Divergent Nickel Catalysis) project will address this challenge by employing a state-of-the-art, multi-scale computational workflow that integrates deep mechanistic investigation with data-driven machine learning (ML). This project will deliver the first comprehensive theoretical investigation into the origins of selectivity in DiNiCa, combined with a novel predictive framework. By integrating Density Functional Theory (DFT) with advanced multireference methods and machine learning, we will construct a robust and predictive mechanistic model. The primary objectives are: 1) to elucidate the complete catalytic cycle and origin of regioselectivity for a key C-C coupling reaction; 2) to unravel the mechanistic basis of enantioselectivity in a challenging hydroamination reaction; and 3) to develop a predictive machine learning model for catalyst selectivity and apply it to the rational in silico design of new, high-performance ligands. By transforming the understanding of these systems from an empirical art to a predictive science, PCoM-RaDeDiNiCa will establish a new paradigm of rational catalyst design. The outcomes will provide the experimental community with a powerful predictive tool and design principles, accelerating the development of more efficient and sustainable catalytic processes with"