In general, Bayesian optimisation relies on three key parts: a probabilistic model (typically a Gaussian Process, GP), an acquisition function and an optimisation algorithm. We first trained a GP using initial experimental data for each objective. The next point to evaluate was selected by maximising an acquisition function 𝛼(𝜒 )./' ). If gradients of 𝛼(𝜒 )./' )are available, gradient-based optimisation algorithms can be utilized. If not, gradients are either approximated or gradient-free methods are used.