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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural scienceschemical sciencesinorganic chemistryorganometallic chemistry
- natural scienceschemical scienceselectrochemistryelectrolysis
- natural scienceschemical sciencesinorganic chemistrytransition metals
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Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Funding Scheme
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinator
0313 Oslo
Norway