Post-doctoral position available in the Institute of Physical Chemistry PAS within FENG.02.02-IP.05-0063/25 „Smart Engineering of Catalysts for Hydrofunctionalization Reactions: From Selectivity Control to a Predictive Model” project financed from the funds of Priority 2 of the European Funds for Smart Economy Program 2021–2027 (FENG) Action 2.2 First Team, with the Intermediate Institution being the Foundation for Polish Science Principal Investigator: dr Dawid Lichosyt Hydrofunctionalizations of alkenes and alkynes—among the most important examples of which are hydrocyanation and hydroformylation—are processes of tremendous industrial importance, enabling the synthesis of pharmaceuticals, polymers, and functional materials.
One of the fundamental limitations of these reactions is the lack of precise control over selectivity.
This leads to the formation of product mixtures, including regio- and stereoisomers as well as isomerization products, which is particularly significant for unsymmetrical substrates.
These issues arise mainly from the limitations of established catalysts based on transition-metal complexes (Ni, Rh) supported by symmetrically constructed phosphine ligands, which do not provide adequate selectivity control.
This project aims to develop a new, holistic approach to controlling selectivity in hydrocyanation and hydroformylation, based on the use of desymmetrized phosphine ligands and advanced artificial intelligence methods.
The planned strategy relies on the synthesis of a broad library of new catalysts (>100) with diverse structures and the evaluation of their catalytic performance in both processes (>1000 substrate–catalyst–conditions combinations).
The resulting experimental dataset on catalyst activity (TON, TOF) and selectivity will be used to develop predictive machine-learning models that enable accurate in silico identification of optimal catalyst–substrate–conditions combinations and the design of new, more selective catalysts.
This will substantially reduce the time required to develop catalysts with the desired selectivity and will lower experimental costs in future practical applications.
The proposed approach has the potential to significantly improve industrial processes, leading to more sustainable and economically efficient technologies for the production of high value-added compounds.
Responsibilities:• Preparation of a catalyst/ligand library and comprehensive structural characterization • Evaluation of catalyst properties across a broad range of substrate–catalyst–conditions combinations, with particular emphasis on the hydrocyanation reaction • Analysis of catalytic reaction outcomes and identification of key selectivity-determining parameters • Mechanistic investigations (e.g., kinetic and spectroscopic studies) to rationalize activity and selectivity trends • Curation, standardization, and quality control of experimental datasets • Preparation of reports, presentation of results, and dissemination (conference presentations, manuscripts