This project will develop state-of-the-art atomistic machine learning (ML) techniques to predict electronic properties and spectroscopic fingerprints directly from atomic structure. The work will involve curating and generating high-quality quantum-mechanical datasets, and designing ML methodologies capable of capturing these different materials properties. The goal is to establish transferable models that link atomic-scale structure to macroscopic behaviour, providing fundamental insights into structure–property relationships in functional materials and accelerating their discovery and optimisation.
The description above outlines a possible new research project being offered to prospective new postgraduate students.
For full details of all postgraduate research projects available for new students and how to apply, please see postgraduate projects available.
Note that post-doctoral research positions are advertised under "Work with Us"
