This work studies how the benefits of automation and machine learning can be applied to the creation, imaging and image analysis of scanning transmission electron microscopy (STEM) samples. Recrystallised tungsten tips are produced using a semi-automated multi-stage process for use as sample platforms in atomic electron tomography (AET). Two coating techniques are tested to see whether they may be viable methods of reducing sample oxidation. An automated microscope control software framework is presented and demonstrated in three different scenarios: the high-throughput acquisition of CdSe/CdS core-shell nanoparticles, the acquisition of CBED patterns of chiral tellurium nanoparticles and the search for candidate particles for alpha tomography. Finally, machine learning is used to classify the handedness of simulated chiral particles using stereopairs of simulated STEM projections. A 'weak labelling' approach is also demonstrated that takes advantage of the intrinsic nature of chirality to remove the need for manually labelling training datasets
