HITS-SIMPLAIX Joint Colloquium Michele Parrinello: Do we really understand catalysis?

By Michele Parrinello, Italian Institute of Technology, Genova (Italy)

The transition to a green economy depends critically on the development of efficient catalysts that could for instance help in sequestrating CO2 or producing H2. To reach this goal a detailed understanding of the catalytic mechanism would be of great value. Unfortunately, most industrial catalysis takes place at temperatures and pressures, so high that both experimental and theoretical investigations are challenging. Current theories cannot explain the extreme durability and stability of industrial catalysts under these conditions. In our view the root of this difficulty lies in a static view of the catalytic process in which the catalyst is only seen as a static platform for reaction. We argue instead that industrial catalysts are dynamical entities that, under reaction conditions evolve forming reactive sites with the reagents. Recent technical development, driven by machine learning methodologies, allow realistic and accurate atomistic simulations to be performed. These simulations are validating such a complex dynamical picture, and we find in our simulations that efficient catalysts are better described as functional materials. We focus our attention on the synthesis and cracking of NH3. The final purpose of these studies is twofold. On the one hand one wants to reduce the remarkably large carbon footprint of ammonia production which account for 4% of the global CO2 emission. On the other, having in mind a hydrogen-based economy indicating ways of dissociating ammonia into its constituents nitrogen and hydrogen so as to allow using ammonia to store and transport hydrogen.

Short CV:

Michele Parrinello currently leads the atomistic simulation group at the Italian Institute of Technology in Genoa. Together with Roberto Car he introduced the ab-initio molecular dynamics method. This method, which goes under the name of Car-Parrinello Method, represents the beginning of a new field and has dramatically influenced the field of electronic structure calculations for solids, liquids and molecules. He is also known for a variety of widely used molecular dynamics methods like the Parrinello-Rahman barostat, metadynamics and more recently he has pioneered the use of machine learning methodologies to molecular dynamics. Parrinello’s scientific interests are strongly interdisciplinary and include the study of complex chemical reactions, materials science and protein dynamics. He has been awarded numerous prizes, among which the 2011 Marcel Benoist Prize, the 2017 Dreyfus Prize and the 2020 Benjamin Franklin Award. He is a member of several academies, and his work is highly cited.

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