  We report the development and implementation of a hypothesis learning based
automated experiment, in which the microscope operating in the autonomous mode
identifies the physical laws behind the material's response. Specifically, we
explore the bias induced transformations that underpin the functionality of
broad classes of devices and functional materials from batteries and memristors
to ferroelectrics and antiferroelectrics. Optimization and design of these
materials require probing the mechanisms of these transformations on the
nanometer scale as a function of the broad range of control parameters such as
applied potential and time, often leading to experimentally intractable
scenarios. At the same time, often the behaviors of these systems are
understood within potentially competing theoretical models, or hypotheses.
Here, we develop a hypothesis list that covers the possible limiting scenarios
for the domain growth, including thermodynamic, domain wall pinning, and
screening limited. We further develop and experimentally implement the
hypothesis driven automated experiment in Piezoresponse Force Microscopy,
autonomously identifying the mechanisms of the bias induced domain switching.
This approach can be applied for a broad range of physical and chemical
experiments with relatively low dimensional control parameter space and for
which the possible competing models of the system behavior that ideally cover
the full range of physical eventualities are known or can be created. These
include other scanning probe microscopy modalities such as force distance curve
measurements and nanoindentation, as well as materials synthesis and
optimization.
