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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.

• Preparation of a catalyst/ligand library and comprehensive structural characterization
• Evaluation of catalyst properties across a broad range of substrate–catalyst–conditions combinations
• 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
• Standardization and quality control of experimental datasets
• Development and validation of machine-learning models for in silico prediction of optimal catalyst–substrate–conditions combinations
• Iterative, model-guided design of improved catalysts and experimental verification of predicted performance
• Preparation of reports, presentation of results, and dissemination (conference presentations, manuscripts

We offer:

- a scholarship position in the Institute of Physical Chemistry PAS with a monthly stipend of 7000 PLN  financed from FIRST TEAM FENG No. FENG.02.02-IP.05-0063/25 project, which is financed from the funds of Priority 2 of the European Funds for a Smart Economy 2021–2027 Program (FENG) Action 2.2 First Team, with the Intermediate Institution being the Foundation for Polish Science.

The stipend is planned for 46 months.  

- the opportunity to work in an interdisciplinary, international research team

- engagement into an ambitious research program and possibility to build the academic career

The successful candidate will be eligible for all programs at the IPC PAS, including the IPC PAS Young Scientists competition.

The selected candidate will join a highly experienced, multidisciplinary team working at the interface of catalysis, organic synthesis, and data-driven chemistry. They will have the opportunity to develop advanced experimental expertise in hydrocyanation and hydroformylation (including catalyst/ligand library synthesis, catalytic screening, and mechanistic studies) and to gain practical experience in building machine-learning models for activity/selectivity prediction and catalyst design. The project will enable them to address selectivity challenges of direct industrial relevance and contribute to more sustainable, economically efficient catalytic technologies

At the starting date of the work within the project, the applicant should be PhD student at a doctoral school.

A MSc degree in chemistry (or a closely related discipline) is required, as well as a strong interest in chemical synthesis and catalysis and in the application of machine learning methods in chemistry. The candidate is expected to be highly motivated for scientific work, capable of independent problem solving, able to work effectively in a team, and systematic and reliable in conducting experiments. Prior experience in organic synthesis or coordination chemistry, basic familiarity with computational tools / programming (e.g. Python), and good command of English sufficient for reading and understanding scientific literature will be considered an advantage.

The committee will consider the following criteria:

• Competence to carry out specific tasks in the research project
• Previous scientific achievements
• Fluency in English