We then initiate our optimisation process with quasi-random Sobol sampling to select initial experiments, aiming to sample experimental configurations diversely spread across the reaction condition space (Fig. ) (see Methods). By maximising reaction space coverage of the initial experiments, Sobol sampling increases the likelihood of discovering informative regions containing optima. Using this initial experimental data, we train a Gaussian Process (GP) regressor to predict reaction outcomes (e.g., yield, selectivity) and their uncertainties for all reaction conditions. An acquisition function, balancing between exploration of unknown (uncertain) regions of the search space and exploitation of previous experiments, then evaluates all reaction conditions and selects the most promising next batch of experiments (see Methods for more details). After obtaining new experimental data, the chemist can choose to repeat this process for as many iterations as desired (Fig. ), usually terminating upon convergence, stagnation in improvement, or exhaustion of experimental budget.