My research lies at the intersection of atomistic modelling, quantum mechanics, and machine learning (ML). I develop data-driven frameworks to predict macroscopic materials properties directly from atomic structure. Using state-of-the-art atomistic ML approaches, I aim to build transferable models that capture electronic, dielectric, and spectroscopic behaviour across functional materials, enabling a deeper understanding of how atomic-scale structure governs materials performance.
