Project First Team

Smart Engineering of Catalysts for Hydrofunctionalization Reactions: From Selectivity Control to a Predictive Model

Principal Investigator: Dr. Dawid Lichosyt
Project No.: FENG.02.02-IP.05-0063/25
Project duration: January 2026 – December 2029

This project is funded under the First Team programme of the Foundation for Polish Science (FNP) within the European Funds for a Smart Economy (FENG) 2021–2027 programme (Priority 2), with a total budget of PLN 3,979,998 fully supported by the European Funds. The First Team programme supports ambitious research projects led by early-career scientists that combine excellent fundamental research with the potential for practical applications and commercialization.

Project overview

Hydrofunctionalization reactions, particularly hydrocyanation and hydroformylation, are among the most important catalytic processes in modern chemical synthesis. They provide efficient routes to nitriles and aldehydes, which serve as key intermediates in the production of pharmaceuticals, agrochemicals, polymers, and functional materials. Despite their industrial importance, these reactions still suffer from a major limitation: achieving precise control over regioselectivity and stereoselectivity, especially for unsymmetrical alkenes and alkynes.

The goal of this project is to develop a general strategy for controlling selectivity in hydrofunctionalization reactions by combining advanced catalyst design with artificial intelligence and machine learning. The project brings together synthetic chemistry, homogeneous catalysis, chemoinformatics, and data science to create predictive tools that will transform the way catalytic systems are designed.

A central objective is the development of a new generation of unsymmetrical phosphine ligands capable of providing fine control over the steric and electronic environment of nickel- and rhodium-based catalysts. More than one hundred new catalysts will be synthesized and systematically evaluated in hydrocyanation and hydroformylation reactions involving a diverse library of unsymmetrical substrates. Their activity, efficiency, and regio- and stereoselectivity will be comprehensively characterized, generating one of the largest experimental datasets available for these transformations.

The experimental results will be integrated with molecular descriptors describing both catalyst and substrate structures to train machine learning models capable of predicting catalyst performance and identifying optimal catalyst–substrate combinations. Ultimately, the project will deliver a chemoinformatics platform that enables rapid, data-driven selection and rational design of highly selective catalysts before experimental validation.

By integrating experimental chemistry with predictive artificial intelligence, the project aims to significantly reduce trial-and-error experimentation, shorten catalyst development time, lower research costs, and minimize chemical waste. The resulting technologies have strong potential for applications in the pharmaceutical, fine chemical, and materials industries, contributing to more sustainable and economically efficient catalytic processes.

The project is also expected to generate a library of innovative catalytic systems, predictive machine learning models, high-impact scientific publications, patent applications, and software tools supporting catalyst design, strengthening the role of artificial intelligence in the future of chemical research.