Matteo Maestri’s research aims to connect fundamental chemical understanding with practical process design through advanced modelling frameworks. His work focuses on capturing the interplay between catalytic reactions and reactor operation across multiple scales, from atomistic mechanisms to industrial systems. 

Within CORC, he leads the project “Physically-informed digital twins for carbon capture and sequestration into advanced materials via methane pyrolysis" in collaboration with Mauro Bracconi.

The project develops physically-informed digital twins - hybrid models that integrate first-principles simulations with machine learning. These models retain physical consistency while enabling fast, predictive simulations, creating a powerful tool for reactor design, process optimization, and scale-up.