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

CORDIS · observation · Publication date unknown

ding 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

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recordType

award

status

SIGNED

region

EU

value

251578.56

unit

EUR