Bayesian optimisation has also been applied to accelerate the discovery of multicomponent systems, for example those containing large numbers of elements, which present a challenge when relying on chemical intuition. Wahl et al. used Bayesian optimisation to discover biphasic, single-interface nanoparticles in the eight-dimensional Au-Ag-Cu-Co-Ni-Pd-Sn-Pt phase space. They first benchmarked their approach on a curated dataset of 148 unique nanoparticle compositions, spanning a range of chemical diversity and interfacial complexity (from 0 to 6 interfaces), using a domain-specific upper confidence bound acquisition function and elemental properties as descriptors. When applying their method in a closed-loop synthesis and characterisation framework, they observed a high success rate with 18 out of 19 candidates resulting in a successful synthesis. Furthermore, they identified extremely complex biphasic nanoparticles that would have unlikely been suggested by a human researcher.