A step towards intelligent EBSD microscopy: machine-learning prediction of twin activity in MgAZ31.

Unlabelled: Although microscopy is often treated as a quasi-static exercise for obtaining a snapshot of events and structure, it is clear that a more dynamic approach, involving real-time decision making for guiding the investigation process, may provide deeper insights, more efficiently. On the other hand, many applications of machine learning involve the interpretation of local circumstances from experience gained over many observations; that is, machine learning potentially provides an ideal solution for more efficient microscopy. This paper explores the potential for informing the microscope's observation strategy while characterising critical events.