My main research interest lies in the application of crystal structure prediction techniques to the characterisation of potential electrode materials for next-generation rechargable batteries. I work primarily on the development of methods and software tools to handle the large volume of relaxed structures generated by ab initio random structure searching (AIRSS) and related high-throughput methods, with an emphasis on energy storage applications.
For a given chemical system, the AIRSS method can be used to map out the potential energy surface by generating many sensible random structures and relaxing them to the nearest stable minima at the DFT level. By varying the composition of the generated structures (e.g. addition of lithium), reaction pathways can be uncovered, and the electrochemical properties of the system as a whole can be studied. In collaboration with experimentalists, AIRSS can be used to provide a database of candidate
phases that can be automatically screened against in situ spectroscopy to find a "best guess" at the local structure during battery use. Reversing the roles, AIRSS is increasingly used to predict the phases of novel materials, again screening for beneficial properties (e.g. high operating voltages, or large capacities). Performing these experiments "in silico" hopefully accelerates the development of the high capacity, safe batteries required to enable a sustainable future.