Density functional theory (DFT) is a quantum mechanical computational modelling method that provides an alternative to solving the many-body Schrodinger equation for the ground-state electron density and energy among other quantities. Central to DFT are the density functional approximations used in practical calculations. My research concerns the application of machine learning strategies to improve these density functional approximations, with a focus on kinetic energy functionals that are used in orbital-free DFT.
