Beyond synthesis, chemical and physical characterisation is another high-dimensional problem that requires significant time even for domain experts. Scanning probe microscopes are powerful instruments to study the properties of materials on the nanoscale. The availability of programmable interfaces for microscopes has enabled automated analysis and operation powered by machine learning. For example, recent work applied deep reinforcement learning to discover how to manipulate individual Ag adatoms on a silver surface, leading to an autonomous atomic assembly system. When operating scanning microscopes, one has to make decisions over i) the instrument parameters (e.g., the set point value and driving amplitude) that can modify the spacial resolution and smearing of the spectra, ii) the scan trajectory used to rasterise the image, and iii) the identification of microstructural elements that possess the behaviors of interest. Bayesian optimisation can be used to help optimise microscopy experiments across all three considerations. Liu et al. employed Bayesian optimisation, combined with deep kernel learning, for the automated discovery of the structure-property relationships in a PbTiO 3 ferroelectric thin film. By learning to map the local domain structure to the corresponding hysterisis loop, their approach was able to identify ferroeletric and ferroelastic domain walls as the origin of ferroelectric behaviour without any prior physical knowledge (Fig. ). While their particular system was studied using piezoresponse force microscopy, the general approach can be applied to any other probe method including optical and mass-spectrometric chemical imaging methods and scanning transmission electron microscopy.