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Predictive Computational Modelling for the Rational Design of Divergent Nickel Catalysis

CORDIS · observation · Publication date unknown

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 rati

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recordType

award

status

SIGNED

region

EU

value

260347.92

unit

EUR