Underlying the rapidly increasing photovoltaic efficiency
and stability
of metal halide perovskites (MHPs) is the advancement in the understanding
of the microstructure of polycrystalline MHP thin film. Over the past
decade, intense efforts have been aimed at understanding the effect
of microstructures 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 films. 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 they require manual operation
by human operators, and 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 have IV curves at all GB locations, allowing
us to systematically understand the property of GBs. Using this method,
we discovered that the GB junction points are less conductive, potentially
more photoactive, and can play critical roles in MHP stability, while
most previous works only focused on the difference between GB and
grains.