My interests lie in the applications of Bayesian unsupervised machine learning techniques within soft matter physics.
My recent work has focused on deciphering the correlations within concentrated electrolytes. Specifically, I am using Bayesian hypothesis testing to deduce whether the bulk of such solutions can be considered homogeneous, or whether, statistically speaking, multiple local ionic environments are present within the solution. A similar technique can be used to look at electrolytes near charged surfaces, which is relevant for the design of energy storage devices. Observing the effect that parameters such as the choice of solvent, ion concentration, or applied voltage across the electrodes, have on these correlations will lead to a better understanding of electrolytic solutions. It should also enable the choice of solvent and ion concentration to be optimised, for example to maximise efficiency or lifetime of a particular energy storage device.