A Machine Learning pipeline to track the dynamics of a population of nanoparticles during in situ Environmental Transmission Electron Microscopy in gases

Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook

“…In fact, meaningful results were obtained just with U-Net both in liquid and gas phases, and under temperature changes, surpassing the former traditional and unsupervised approaches. [174][175][176] In any case, it is clear that ML has much more to say in in situ EM, and it will surely explode as soon as the in situ machinery becomes a much wider standard within the community. For instance, we envision the use of CS to reduce the required frame rate, similarly to CS-tomography, further allowing the chemical and physical tracking of beam-sensitive materials.…”