About
I am a Research Associate in the Department of Chemistry at the University of Liverpool, working at the intersection of computational materials science and artificial intelligence.
My research integrates first-principles simulation and machine learning to accelerate the discovery and design of functional materials.
I develop methods that link atomic-scale physics with large-scale data modelling — from density-functional theory (DFT) and molecular dynamics to probabilistic and generative AI models. This multiscale perspective connects the periodic table, chemical composition, crystal structure, and electronic behaviour, enabling the prediction of materials with tailored properties.
My current work focuses on AI-guided materials discovery, including active-learning and Bayesian-optimisation frameworks for compositional search, and physics-informed generative models for crystal-structure design. These approaches have led to experimentally verified materials discoveries, as reported in Science and Nature Communications, and to a pending UK patent.
I enjoy collaborating across chemistry, physics, and computer science to translate computational advances into practical discovery tools.
Here is a highlight of my work by Scientific American.
Check out my projects at GitHub.