To demonstrate the potential of optimisation in physical science, we apply Bayesian optimisation to a photovoltaic device problem. The Modelling Solar Cells package doping concentrations ∈ [10 14 , 10 18 ] cm -3 ). Accordingly, even using very coarse sampling of the parameter space, the grid search approach must still sample 400 points for complete coverage. In contrast, it takes less than 20 iterations for Bayesian optimisation to converge to maximum efficiency. In Fig. , we compare the performance of Bayesian optimisation against random sampling with respect to the number of iterations. Due to the stochastic nature of the optimisation, we repeat the experiment 30 times and plot the standard deviation of predictions in the shaded region. While Bayesian optimisation converges rapidly to the optimal value (17.1 %), random sampling converges much more slowly and only reaches an efficiency of 14.5 % after 75 iterations. In Figs. we illustrate two-dimensional slices of the optimisation surfaces, highlighting the route Bayesian optimisation samples the parameter space. The optimisation algorithm quickly identifies a p-type thickness of 10 -5 m and ntype carrier concentration of 10 18 cm -3 as optimal values, and samples the remaining input parameters to find the global maximum. Our results demonstrate the potential of Bayesian optimisation compared to naive grid and random searches.