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.