Abstract

Efficient catalysts are needed are needed to produce large quantity of commodity chemicals and to facilitate the transition to a green economy. In industry these processes   take place at temperatures and pressures, so high that experimental and theoretical investigations  are challenging. Current theories cannot explain the extreme durability and stability of industrial catalyst. Thanks to the application of machine learning to molecular dynamics simulations in combination with enhanced sampling methodologies we are in the position to perform ab-initio quality simulations of the operando catalytic process.  We focus our attention on catalysts that can synthesize or decompose NH₃. These include the iron based Haber-Bosh catalyst, and transition-metal free catalysts such as BaH₂ and a variety of amidic compounds like Li₂NH.  In line with earlier speculations, we find that the establishment of a stable and durable catalytic process is to be associated to temperature induced surface mobility and reactant induced surface modifications.

References

Spencer M., Nature 1986, 323, 685; Schuth F., Palkovits R., Schlögl R., Su D. S., Energy & Environmental Science 2012, 5, 6278; Schlögl R., Angewandte Chemie International Edition 2015, 54, 3465; Yang M., Raucci U., Parrinello M., Nature Catalisys 2023, 6, 829;  Mambretti F., Raucci, U., Yang M., Parrinello M., ChemRxiv, 2023;  Bonati L., Polino D., Pizzolitto C., Biasi P., Eckert R., Reitmeier S., Schlögl R., Parrinello M., PNAS, 2023, just accepted; Tripathi  F., Bonati L., Perego S., Parrinello M., ChemRxiv , 2023.