Dr. Donald Hlungwani is a postdoctoral fellow at the Materials Modelling Centre of the University of Limpopo. He received his master’s and PhD in physics from the University of Limpopo, where his research focused on multiscale modelling of manganese-rich cathode materials for lithium-ion batteries.

The study primarily involved molecular dynamics (MD) simulations outside the Materials Exploration and Design Analysis (MedeA) environment, and density functional theory (DFT) within the MedeA environment using the Vienna Ab-initio Simulation Package (VASP). His PhD work also included the derivation of Buckingham interatomic potentials using machine-learning-inspired optimization techniques to fit DFT potential energy curves to parameterized potential energy functions.

In his current postdoctoral work, he employs the Materials Design software to train machine learning force fields on-the-fly from ab-initio MD trajectories, with the aim of exploring the transport properties of cathode and solid electrolyte materials for all-solid-state (ASS) lithium-ion batteries.