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We report the development and implementation of a hypothesis learning basedautomated experiment, in which the microscope operating in the autonomous modeidentifies the physical laws behind the material's response. Specifically, weexplore the bias induced transformations that underpin the functionality ofbroad classes of devices and functional materials from batteries and memristorsto ferroelectrics and antiferroelectrics. Optimization and design of thesematerials require probing the mechanisms of these transformations on thenanometer scale as a function of the broad range of control parameters such asapplied potential and time, often leading to experimentally intractablescenarios. At the same time, often the behaviors of these systems areunderstood within potentially competing theoretical models, or hypotheses.Here, we develop a hypothesis list that covers the possible limiting scenariosfor the domain growth, including thermodynamic, domain wall pinning, andscreening limited. We further develop and experimentally implement thehypothesis 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 chemicalexperiments with relatively low dimensional control parameter space and forwhich the possible competing models of the system behavior that ideally coverthe full range of physical eventualities are known or can be created. Theseinclude other scanning probe microscopy modalities such as force distance curvemeasurements and nanoindentation, as well as materials synthesis andoptimization.

Hypothesis-Driven Automated Experiment in Scanning Probe Microscopy: Exploring the Domain Growth Laws in Ferroelectric Materials