To understand the performance of Bayesian optimisation on materials science problems, Liang et al. compiled five datasets from the literature (Fig. ) spanning polymeric and nanoparticle formulations, inorganic thin films, and materials manufacturing, with a range of dimensions (3-5) and sample counts (94-600). For each dataset, they evaluated the per-formance of Bayesian optimisation against random sampling at discovering the percentage of top candidates and the improvement in objective function. Bayesian optimisation using Gaussian processes with anisotropic kernels and random forests as the surrogate model were found to outperform random sampling in all cases. Optimisation of chemical reactions is a highly complex, multidimensional problem that requires synthetic chemists to evaluate many parameters, including reagents, concentrations, temperatures, solvents, substrates, and catalysts. Bayesian optimisation has emerged as a powerful tool in facilitating the efficient synthesis of functional chemicals. 5,55, Shields et al. developed a Bayesian approach using quantum chemical properties of reaction components as descriptors. They benchmarked their method on a large Pd-catalysed direct arylation reaction dataset and the real-world optimisation of Mitsunobu and deoxyfluorination reactions. Their approach was found to outperform human decision-making on both average optimisation efficiency (i.e., the total number of experiments needed to optimise the reaction) and consistency (i.e., the variance of the final performance against initially available data). Their work highlights the potential of integrating autonomous experiment planning systems into chemical robotics platforms.