My PhD Research in Computational Chemistry

I completed my PhD under the supervision of Dr Andrew Logsdail at the Cardiff Catalysis Institute, in collaboration with Johnson Matthey and the bp International Centre for Advanced Materials (bp-ICAM), as part of the EPSRC-funded Prosperity Partnership Sustainable Catalysis for Clean Growth.

My thesis is titled "Physics-Informed Machine Learning for Modelling Defect-Driven Catalytic Phenomena", focusing on the development and application of machine learning, statistical algorithms and quantum mechanical simulations to better understand how defects influence the performance of complex catalytic materials:

• Developing new methods for determining DFT+U parameters (U values and projectors) that enable accurate and efficient simulations of polarons in transition metal and rare-earth oxides.
• Applying DFT-parameterised statistical algorithms (Monte Carlo sampling) and interatomic potentials (MACE) to investigate design strategies for sulfur-tolerant industrial catalysts.

These and other ongoing projects were highly collaborative and would not have been possible without the expertise of several theoretical researchers at Cardiff University and developers of the FHI-aims electronic structure package; and experimental researchers in the groups of Dr Andrea Folli (EPR spectroscopy, Cardiff University), Professor Sarah Haigh (TEM microscopy, University of Manchester) and Johnson Matthey (Advanced Characterisation Department).