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Controlling the Structure of Transition-Metal Catalysts by Tuning their Chemical Environment

Project description

New tools to explore the behaviour of transition metal catalysts

Transition metal catalysts are essential in both research and industry, but their efficiency depends heavily on their chemical environment. With the support of the Marie Skłodowska-Curie Actions programme, the Deep-Cat project aims to further understand and optimise how factors such as solvents, additives and ligands affect catalytic reactivity. Deep-Cat will also investigate how palladium and nickel catalysts interact with their environments to improve reaction selectivity and efficiency. The project will significantly enhance the project fellow’s expertise in multi-scale molecular modelling and machine learning methods, establishing them as a key player in the application of advanced computational techniques. These methods will help establish new standards in homogeneous catalysis research.

Objective

This project aims to develop an understanding of how the chemical environment (i.e. solvent, additive and ligand) can be adapted to tune the reactivity of transition metal catalysts, based on molecular dynamics modelling methods enhanced by machine learning. Pd-catalysed cross-coupling reactions are widespread in research and industry. The efficiency of these processes depends on a detailed molecular understanding of the catalytic cycles, which are highly sensitive to chemical environmental factors. The identification of active species in reaction mixtures is crucial, as demonstrated in the case of Pd-catalysed oxidative addition (Liang et Al., Inorg. Chem. 2021; Rio et Al., ACS Catal. 2023). This project employs deep-neural network potentials to address the challenges of nuclearity and reactivity commonly encountered in homogeneous catalysis, with a particular focus on the interaction of the catalyst with its chemical environment. The initial phase will investigate the speciation of the Pd(OAc)2 pre-catalyst in solvents of varying polarity, with the objective of controlling its nuclearity. Subsequently, these species will be subjected to further analysis at a static DFT level in oxidation reactions. The second part of the project will examine how the ratio of palladium to phosphine affects the nuclearity of palladium catalysts and the selectivity of cross-coupling reactions (Scott et al., JACS 2020). The last stage of the project, in collaboration with an international experimental leader, will broaden the project to investigate the role played by the chemical environment in Ni-catalysed single-electron transfer reactions (Tilley et al., Organometallics 2021). This project will significantly enhance the expertise of the applicant in multi-scale molecular modelling and machine learning methods, establishing them as a key player in the application of advanced computational techniques to address the diversity of challenges encountered in homogeneous catalysis.

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Programme(s)

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Topic(s)

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Funding Scheme

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

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) HORIZON-MSCA-2024-PF-01

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Coordinator

UNIVERSITETET I OSLO
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.

€ 251 578,56
Address
PROBLEMVEIEN 5-7
0313 Oslo
Norway

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Region
Norge Oslo og Viken Oslo
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

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