Designing high entropy perovskites for sustainable ammonia synthesis using advanced computational techniques and artificial intelligence

Ammonia is the second most-produced chemical globally, most of which is used in the fertilizers industry. Currently, it is synthesized via the Haber-Bosch process operated at high temperature and pressure using a Fe-based catalyst, which contributes to the generation of 420 million tons of CO2 per year. Therefore, researchers worldwide are currently looking for inexpensive and efficient catalyst for sustainable ammonia production. Perovskites have been studied as effective, low-cost substitutes to expensive noble-metals-based catalysts. Different compositions of transition metals can be used in a perovskite material, making their physical and chemical properties highly flexible and providing a vast searching space for the potential High Entropy Perovskite (HEP) catalysts. This large area of study can benefit a lot from Machine Learning techniques. Until today, there have been only a few studies that analyzed perovskite-type oxides for electrocatalytic Nitrogen Reduction Reaction (NRR), which is one of the rate limiting steps of the ammonia synthesis reaction. The proposed project aims to expedite this research by using artificial intelligence and advanced computational chemistry techniques. We plan to investigate various compositions of HEPs, and manipulate them based on the expected physical and chemical properties of an ideal sustainable ammonia synthesis catalysts.

Faculty Supervisor:

Kulbir Ghuman

Student:

Partner:

Forschungszentrum Jülich

Discipline:

Physics

Sector:

Green/Alternative Energy; Advanced Computing; Artificial Intelligence

University:

Université du Québec : Institut national de la recherche scientifique

Program: