where x ⋆ is the parameter that produces the minimum of the objective function, f , and X is the domain of interest. When considering complex high-dimensional landscapes, there are often many minima (stationary points of the target function or experiment) which make finding the global minimum exceedingly difficult (Fig. ). This process is termed global op-timisation. Examples include hyperparameter optimisation of supervised machine learning algorithms (in this case, the objective function is the mean average error [MAE] or mean squared error [MSE] of the model), 9 solving the geometric structure of proteins and crystal structures (here the objective function is the root mean squared error in the atomic positions), and choosing the architecture and synthesis conditions for functional devices (where the objective function is the figure of merit of the device).