PRE2023-Optimizing Catalyst Design through computational chemistry and Machine Learning (PID2022-140159NA-I00)

Industrial chemical manufacturing is a significant source of pollution and significant changes are needed to meet global sustainability goals. Catalytic reactions play a crucial role in these industrial processes, as approximately 80% of modern manufacturing processes involve at least one catalytic reaction. This multidisciplinary project aims to accelerate the discovery of catalysts and improve process efficiency by creating new generations of catalysts. It will advance knowledge in various fields of chemistry and catalysis while addressing challenges from the European Research Area such as investing in research and innovation for a sustainable and digital future, supporting the mobility and career opportunities of researchers, promoting gender equality and diversity, and fostering open science. The Project will target multiple areas of catalyst discovery, such as organocatalysis and metal catalysis, using a synergic approach that merges computational chemistry, machine learning, and experimental chemistry across three work packages (WPs). WP1 will focus on developing new aspects of chiral organocatalysis, WP2 on creating a sustainable organocatalytic synthesis of the widely produced chemical butadiene, and WP3 on discovering a type of controlled metal on/off catalysis in which light triggers the generation of vacancies in the metal center.