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Predictive Computational Modelling for the Rational Design of Divergent Nickel Catalysis

Project description

Computational framework for advanced nickel ligand design

Chemical synthesis using nickel catalysts currently relies on slow, trial-and-error screening because scientists do not fully understand how specific components, known as ligands, govern reaction outcomes. Supported by the Marie Skłodowska-Curie Actions programme, the PCoM-RaDeDiNiCa project aims to transform this empirical guesswork into a predictive science. By combining advanced computer simulations with machine learning, the project will create a digital framework to map the fundamental mechanisms of complex chemical reactions. Key objectives include uncovering how catalysts control product selectivity and developing tools for the rational design of new ligands. Ultimately, this project may establish a new paradigm, providing researchers with the principles needed to accelerate efficient and sustainable catalytic processes.

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"

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HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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(opens in new window) HORIZON-MSCA-2025-PF

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Coordinator

THE UNIVERSITY COURT OF THE UNIVERSITY OF ST ANDREWS
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 260 347,92
Address
NORTH STREET 66 COLLEGE GATE
KY16 9AJ ST ANDREWS
United Kingdom

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Region
Scotland Eastern Scotland Clackmannanshire and Fife
Activity type
Higher or Secondary Education Establishments
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Total cost

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