Dynamic Manipulation in Piezoresponse Force Microscopy: Creating Nonequilibrium Phases with Large Electromechanical Response

Exchange-Interaction-Like Behavior in Ferroelectric Bilayers

Interlayer coupling in materials, such as exchange interactions at the interface between an antiferromagnet and a ferromagnet, can produce exotic phenomena not present in the parent materials. While such interfacial coupling in magnetic systems is widely studied, there is considerably less work on analogous electric counterparts (i.e., akin to electric "exchange-bias-like" or "exchange-spring-like" interactions between two polar materials) despite the likelihood that such effects can also engender new features associated with anisotropic electric dipole alignment. Here, electric analogs of such exchange interactions are reported, and their physical origins are explained for bilayers of in-plane polarized Pb1-x Srx TiO3 ferroelectrics. Variation of the strontium content and thickness of the layers provides for deterministic control over the switching properties of the bilayer system resulting in phenomena analogous to an exchange-spring interaction and, leveraging added control of these interactions with an electric field, the ability to realize multistate-memory function. Such observations not only hold technological promise for ferroelectrics and multiferroics but also extend the similarities between ferromagnetic and ferroelectric materials to include the manifestation of exchange-interaction-like phenomena.

Physics Discovery in Nanoplasmonic Systems via Autonomous Experiments in Scanning Transmission Electron Microscopy

Physics-driven discovery in an autonomous experiment has emerged as a dream application of machine learning in physical sciences. Here, this work develops and experimentally implements a deep kernel learning (DKL) workflow combining the correlative prediction of the target functional response and its uncertainty from the structure, and physics-based selection of acquisition function, which autonomously guides the navigation of the image space. Compared to classical Bayesian optimization (BO) methods, this approach allows to capture the complex spatial features present in the images of realistic materials, and dynamically learn structure-property relationships. In combination with the flexible scalarizer function that allows to ascribe the degree of physical interest to predicted spectra, this enables physical discovery in automated experiment. Here, this approach is illustrated for nanoplasmonic studies of nanoparticles and experimentally implemented in a truly autonomous fashion for bulk- and edge plasmon discovery in MnPS₃ , a lesser-known beam-sensitive layered 2D material. This approach is universal, can be directly used as-is with any specimen, and is expected to be applicable to any probe-based microscopic techniques including other STEM modalities, scanning probe microscopies, chemical, and optical imaging.

Probing Metastable Domain Dynamics via Automated Experimentation in Piezoresponse Force Microscopy

The dynamics of complex topological defects in ferroelectric materials is explored using automated experimentation in piezoresponse force microscopy. Specifically, a complex trigger system (i.e., "FerroBot") is employed to study metastable domain-wall dynamics in Pb_0.6Sr_0.4TiO₃ thin films. Several regimes of superdomain wall dynamics have been identified, including smooth domain-wall motion and significant reconfiguration of the domain structures. We have further demonstrated that microscopic mechanisms of the domain-wall dynamics can be identified; i.e., domain-wall bending can be separated from irreversible domain reconfiguration regimes. In conjunction, phase-field modeling was used to corroborate the observed mechanisms. As such, the observed superdomain dynamics can provide a model system for classical ferroelectric dynamics, much like how colloidal crystals provide a model system for atomic and molecular systems.

Automated and Autonomous Experiments in Electron and Scanning Probe Microscopy

Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent successes in applying ML/AI methods for autonomous systems from robotics to self-driving cars to organic and inorganic synthesis are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiments (AE) in imaging. Here, we aim to analyze the major pathways toward AE in imaging methods with sequential image formation mechanisms, focusing on scanning probe microscopy (SPM) and (scanning) transmission electron microscopy ((S)TEM). We argue that automated experiments should necessarily be discussed in a broader context of the general domain knowledge that both informs the experiment and is increased as the result of the experiment. As such, this analysis should explore the human and ML/AI roles prior to and during the experiment and consider the latencies, biases, and prior knowledge of the decision-making process. Similarly, such discussion should include the limitations of the existing imaging systems, including intrinsic latencies, non-idealities, and drifts comprising both correctable and stochastic components. We further pose that the role of the AE in microscopy is not the exclusion of human operators (as is the case for autonomous driving), but rather automation of routine operations such as microscope tuning, etc., prior to the experiment, and conversion of low latency decision making processes on the time scale spanning from image acquisition to human-level high-order experiment planning. Overall, we argue that ML/AI can dramatically alter the (S)TEM and SPM fields; however, this process is likely to be highly nontrivial and initiated by combined human-ML workflows and will bring challenges both from the microscope and ML/AI sides. At the same time, these methods will enable opportunities and paradigms for scientific discovery and nanostructure fabrication.

Phase Transition Effect on Ferroelectric Domain Surface Charge Dynamics in BaTiO3 Single Crystal

The ferroelectric domain surface charge dynamics after a cubic-to-tetragonal phase transition on the BaTiO₃ single crystal (001) surface was directly measured through scanning probe microscopy. The captured surface potential distribution shows significant changes: the domain structures formed rapidly, but the surface potential on polarized c domain was unstable and reversed its sign after lengthy lapse; the high broad potential barrier burst at the corrugated a-c domain wall and continued to dissipate thereafter. The generation of polarization charges and the migration of surface screening charges in the surrounding environment take the main responsibility in the experiment. Furthermore, the a-c domain wall suffers large topological defects and polarity variation, resulting in domain wall broadening and stress changes. Thus, the a-c domain wall has excess energy and polarization change is inclined to assemble on it. The potential barrier decay with time after exposing to the surrounding environment also gave proof of the surface screening charge migration at surface. Thus, both domain and domain wall characteristics should be taken into account in ferroelectric application.

Autonomous Experiments in Scanning Probe Microscopy and Spectroscopy: Choosing Where to Explore Polarization Dynamics in Ferroelectrics

Polarization dynamics in ferroelectric materials are explored via automated experiment in piezoresponse force microscopy/spectroscopy (PFM/S). A Bayesian optimization (BO) framework for imaging is developed, and its performance for a variety of acquisition and pathfinding functions is explored using previously acquired data. The optimized algorithm is then deployed on an operational scanning probe microscope (SPM) for finding areas of large electromechanical response in a thin film of PbTiO₃, with results showing that, with just 20% of the area sampled, most high-response clusters were captured. This approach can allow performing more complex spectroscopies in SPM that were previously not possible due to time constraints and sample stability. Improvements to the framework to enable the incorporation of more prior information and improve efficiency further are modeled and discussed.