to reduce computation time, Gaussian processes are generally limited to problems with less than a few thousand data points. However, in experimental materials science and chemistry domains with relatively small dataset sizes, Gaussian processes are often the surrogate model of choice for Bayesian optimisation due to their relatively small number of hyperparameters and high out-of-the-box accuracy.