PhD in Computational

Machine Learning approaches to whole-particle modelling of Pt nanoparticles for fuel cells/nSupervisor: Professor Nicholas Hine, University of Warwick/nThis project uses cutting-edge computational and machine learning methods to accelerate catalyst discovery for fuel cell technology. Hydrogen fuel cells can produce clean electricity from hydrogen and oxygen, but their performance and cost are limited by platinum-based catalysts. This project will use advanced computational methods to accelerate the discovery of better catalysts that use less platinum and have improved long-term stability./nBy combining large-scale density functional theory with machine-learned interatomic potentials and automated reaction discovery, we will reveal how nanoparticle composition, geometry and electronic structure govern electrochemical reaction rates, helping design efficient, low-cost materials for sustainable hydrogen technologies./nAffiliation: EPSRC Centre for Doctoral Training (CDT) in Modelling of Heterogeneous Systems (HetSys) at the University of Warwick. Location: Coventry, University of Warwick./nFunded as part of an Industrial Doctoral Landscape Awards (IDLA) programme and in partnership with Johnson Matthey.