  Underlying the rapidly increasing photovoltaic efficiency and stability of
metal halide perovskites (MHPs) is the advance in the understanding of the
microstructure of polycrystalline MHP thin film. Over the past decade, intense
efforts have aimed to understand the effect of microstructure on MHP
properties, including chemical heterogeneity, strain disorder, phase impurity,
etc. It has been found that grain and grain boundary (GB) are tightly related
to lots of microscale and nanoscale behavior in MHP thin film. Atomic force
microscopy (AFM) is widely used to observe grain and boundary structures in
topography and subsequently to study the correlative surface potential and
conductivity of these structures. For now, most AFM measurements have been
performed in imaging mode to study the static behavior, in contrast, AFM
spectroscopy mode allows us to investigate the dynamic behavior of materials,
e.g. conductivity under sweeping voltage. However, a major limitation of AFM
spectroscopy measurements is that it requests manual operation by human
operators, as such only limited data can be obtained, hindering systematic
investigations of these microstructures. In this work, we designed a workflow
combining the conductive AFM measurement with a machine learning (ML) algorithm
to systematically investigate grain boundaries in MHPs. The trained ML model
can extract GBs locations from the topography image, and the workflow drives
the AFM probe to each GB location to perform a current-voltage (IV) curve
automatically. Then, we are able to IV curves at all GB locations, allowing us
to systematically understand the property of GBs. Using this method, we
discover that the GB junction points are more photoactive, while most previous
works only focused on the difference between GB and grains.
