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Size-driven transition of an antiferroelectric into a polar ferroelectric or ferrielectric state is a strongly debated issue from both experimental and theoretical perspectives. While critical thickness limits for such transitions have been explored, a bottom-up approach in the ultrathin limit considering few atomic layers could provide insight into the mechanism of stabilization of the polar phases over the antipolar phase seen in bulk PbZrO3. Here, we use first-principles density functional theory to predict the stability of polar phases in Pt/PbZrO3/Pt nanocapacitors. In a few atomic layer thick slabs of PbZrO3 sandwiched between Pt electrodes, we find that the polar phase originating from the well established R3c phase of bulk PbZrO3 is energetically favorable over the antipolar phase originating from the Pbam phase of bulk PbZrO3. The famous triple-well potential of antiferroelectric PbZrO3 is modified in the nanocapacitor limit in such a way as to swap the positions of the global and local minima, stabilizing the polar phase relative to the antipolar one. The size effect is decomposed into the contributions from dimensionality reduction, surface charge screening, and interfacial relaxation, which reveals that it is the creation of well-compensated interfaces that stabilizes the polar phases over the antipolar ones in nanoscale PbZrO3. 

Abstract Piezoresponse force microscopy (PFM) is routinely used to probe the nanoscale electromechanical response of ferroelectric and piezoelectric materials. However, many challenges remain in the interpretation of the recovered signal. Specifically, many non‐ferroelectric contributions affect the measured response, ranging from electrostatics, to charge injection and trapping, and topographic cross‐talk. Recently, machine learning (ML) has been utilized to identify multiple contributors within complex data systems, such as PFM response. A substantial advancement in ML approaches for PFM techniques is offered by dimensional stacking, enabling encoding of physical and/or chemical correlations within the materials' response across different data dimensions spanning varying ranges. However, dimensional stacking requires appropriate scaling for each dimension (before ML analysis) to minimize undesired information loss. Here, the impact of clustering globally and locally scaled parameters in polarization switching experiments via resonant PFM (RPFM) are discussed. Specifically, dimensional stacking of scaled parameters can mask or enhance ferroelectric and non‐ferroelectric behaviors, and aid identification of various physical phenomena contributing to the measured RPFM response. This study highlights the importance of data curation for ML, and its role in identifying signal contributors to scanning probe microscopy (SPM)‐based techniques with multidimensional data, such as resonant and/or spectroscopic SPM. 

Abstract Antiferroelectric materials, where the transition between antipolar and polar phase is controlled by external electric fields, offer exceptional energy storage capacity with high efficiencies, giant electrocaloric effect, and superb electromechanical response. PbZrO₃is the first discovered and the archetypal antiferroelectric material. Nonetheless, substantial challenges in processing phase pure PbZrO₃have limited studies of the undoped composition, hindering understanding of the phase transitions in this material or unraveling the controversial origins of a low‐field ferroelectric phase observed in lead zirconate thin films. Leveraging highly oriented PbZrO₃thin films, a room‐temperature ferrielectric phase is observed in the absence of external electric fields, with modulations of amplitude and direction of the spontaneous polarization and large anisotropy for critical electric fields required for phase transition. The ferrielectric state observations are qualitatively consistent with theoretical predictions, and correlate with very high dielectric tunability, and ultrahigh strains (up to 1.1%). This work suggests a need for re‐evaluation of the fundamental science of antiferroelectricity in this archetypal material. 

Abstract Scanning Probe Microscopy (SPM) based techniques probe material properties over microscale regions with nanoscale resolution, ultimately resulting in investigation of mesoscale functionalities. Among SPM techniques, piezoresponse force microscopy (PFM) is a highly effective tool in exploring polarization switching in ferroelectric materials. However, its signal is also sensitive to sample‐dependent electrostatic and chemo‐electromechanical changes. Literature reports have often concentrated on the evaluation of theOff‐fieldpiezoresponse, compared toOn‐fieldpiezoresponse, based on the latter's increased sensitivity to non‐ferroelectric contributions. Using machine learning approaches incorporatingboth Off‐andOn‐fieldpiezoresponse response as well asOff‐fieldresonance frequency to maximize information, switching piezoresponse in a defect‐rich Pb(Zr,Ti)O₃thin film is investigated. As expected, one major contributor to the piezoresponse is mostly ferroelectric, coupled with electrostatic phenomena duringOn‐fieldmeasurements. A second component is electrostatic in nature, while a third component is likely due to a superposition of multiple non‐ferroelectric processes. The proposed approach will enable deeper understanding of switching phenomena in weakly ferroelectric samples and materials with large chemo‐electromechanical response.