I've always been an explorer at heart, and science has provided me the perfect route for discovering many exciting new fields. In my undergraduate studies I was interested in numerical methods and computational materials science. Professor Prita Pant started me off on Dislocation Dynamics, the study of the motion of defects in crystal structures. It started me off on micromechanics and mainly, the use of computational methods to understand the underlying physics of materials. The method I used most frequently was the Finite Element Method, which essentially applies simplified equations over small subsections of a complex structure. This allows us to numerically solve problems for which analytical solutions do not exist. Using the finite element method I have explored lithium ion batteries, the high velocity response of layered materials and more recently, lithium sulfur batteries.

More recently though I have turned towards empirical and data based methods to understand these phenomena, namely Data Science. In my years of experience with battery science, I have come to realise that the amount of data we generate far outpaces the rate at which we make breakthroughs in converting complex interactions in these systems into mathematical equations. Modern methods such as Machine Learning and Deep Learning could therefore be used to uncover relationships that are not evident easily. These methods will aid current fundamental mechanistic modelling rather than replace them outright.