In this project we will use cutting-edge computational structure prediction techniques to investigate new electrode materials in lithium and sodium ion batteries (LIB/NIB). The theoretical prediction of even very simple structures has, until recently, been out of bounds due to the large number of possible atomic arrangements. The ab initio random structure searching method (AIRSS)  uses a stochastic approach to suggest different structural configurations of atoms within a material. By searching over a range of stoichiometries it is possible to model how a battery is charged as we can predict the structural changes of the electrodes as a charging potential is applied. Using this knowledge it is possible to perform further theoretical analysis such as NMR and EELS (electron energy loss) spectroscopies and predict charge and discharge voltages. AIRSS has been very successful at predicting the structures of lithium silicides, germanides and sulphides along with lithium defects insilicon. Our latest projects involve going beyond these and considering the dynamical processes as the batteries charge and discharge in the anode and cathode using both AIRSS and data-mining techniques. This project is computational and suitable for both the theorist interested in technologically relevant applications and the experimentalist looking for insight into computational structure prediction.