I’m a computational physic specialising in DFT, quantum chemistry, and interpretable machine-learning models for materials discovery. My work focuses on energy-conversion materials, catalytic interfaces, and quantum defects in solids. I combine electronic-structure theory with data-driven approaches to predict properties, uncover mechanisms, and guide experimental development. I collaborate widely across academia and industry to accelerate the design of next-generation functional materials. A key focus of my work is using early-stage modelling to reduce uncertainty and guide practical design choices in real-world engineered systems.