The aim of the project is to advance the field of computational heterogeneous catalysis with the development and application of machine learning methods for accelerating the computational screening of catalytic materials. Specifically, it will target CO₂ conversion over metal oxides, a challenging class of materials that show good catalytic performance but remain largely unexplored with current approaches. Machine learning methods can learn the quantum mechanical principles behind catalysis based on a smaller data set of catalytic systems and then be used to predict other systems efficiently and accurately to establish kinetic models for the catalytic activity and thereby help guide experimental investigations. The project will fund Raffaele’s salary for 3 years as well as computing resources and visits to international collaborators at the Carnegie Mellon University in the USA.