  Electronic transport and hysteresis in metal halide perovskites (MHPs) are
key to the applications in photovoltaics, light emitting devices, and light and
chemical sensors. These phenomena are strongly affected by the materials
microstructure including grain boundaries, ferroic domain walls, and secondary
phase inclusions. Here, we demonstrate an active machine learning framework for
'driving' an automated scanning probe microscope (SPM) to discover the
microstructures responsible for specific aspects of transport behavior in MHPs.
In our setup, the microscope can discover the microstructural elements that
maximize the onset of conduction, hysteresis, or any other characteristic that
can be derived from a set of current-voltage spectra. This approach opens new
opportunities for exploring the origins of materials functionality in complex
materials by SPM and can be integrated with other characterization techniques
either before (prior knowledge) or after (identification of locations of
interest for detail studies) functional probing.
