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Abstract Machine learning (ML) is relied on for materials spectroscopy. It is challenging to make ML models fail because statistical correlations can mimic the physics without causality. Here, using a benchmark band‐excitation piezoresponse force microscopy polarization spectroscopy (BEPS) dataset the pitfalls of the so‐called “better”, “faster”, and “less‐biased” ML of electromechanical switching are demonstrated and overcome. Using a toy and real experimental dataset, it is demonstrated how linear nontemporal ML methods result in physically reasonable embedding (eigenvalues) while producing nonsensical eigenvectors and generated spectra, promoting misleading interpretations. A new method of unsupervised multimodal hyperspectral analysis of BEPS is demonstrated using long‐short‐term memory (LSTM) β‐variational autoencoders (β‐VAEs) . By including LSTM neurons, the ordinal nature of ferroelectric switching is considered. To improve the interpretability of the latent space, a variational Kullback–Leibler‐divergency regularization is imposed . Finally, regularization scheduling of β as a disentanglement metric is leveraged to reduce user bias. Combining these experiment‐inspired modifications enables the automated detection of ferroelectric switching mechanisms, including a complex two‐step, three‐state one. Ultimately, this work provides a robust ML method for the rapid discovery of electromechanical switching mechanisms in ferroelectrics and is applicable to other multimodal hyperspectral materials spectroscopies. 

Abstract Ferroelectrics are being increasingly called upon for electronic devices in extreme environments. Device performance and energy efficiency is highly correlated to clock frequency, operational voltage, and resistive loss. To increase performance it is common to engineer ferroelectric domain structure with highly‐correlated electrical and elastic coupling that elicit fast and efficient collective switching. Designing domain structures with advantageous properties is difficult because the mechanisms involved in collective switching are poorly understood and difficult to investigate. Collective switching is a hierarchical process where the nano‐ and mesoscale responses control the macroscopic properties. Using chemical solution synthesis, epitaxially nearly‐relaxed (100) BaTiO₃films are synthesized. Thermal strain induces a strongly‐correlated domain structure with alternating domains of polarization along the [010] and [001] in‐plane axes and 90° domain walls along the [011] or [01] directions. Simultaneous capacitance–voltage measurements and band‐excitation piezoresponse force microscopy revealed strong collective switching behavior. Using a deep convolutional autoencoder, hierarchical switching is automatically tracked and the switching pathway is identified. The collective switching velocities are calculated to be ≈500 cm s⁻¹at 5 V (7 kV cm⁻¹), orders‐of‐magnitude faster than expected. These combinations of properties are promising for high‐speed tunable dielectrics and low‐voltage ferroelectric memories and logic. 

Abstract Ferroelectric materials exhibit spontaneous polarization that can be switched by electric field. Beyond traditional applications as nonvolatile capacitive elements, the interplay between polarization and electronic transport in ferroelectric thin films has enabled a path to neuromorphic device applications involving resistive switching. A fundamental challenge, however, is that finite electronic conductivity may introduce considerable power dissipation and perhaps destabilize ferroelectricity itself. Here, tunable microwave frequency electronic response of domain walls injected into ferroelectric lead zirconate titanate (PbZr_0.2Ti_0.8O₃) on the level of a single nanodomain is revealed. Tunable microwave response is detected through first‐order reversal curve spectroscopy combined with scanning microwave impedance microscopy measurements taken near 3 GHz. Contributions of film interfaces to the measured AC conduction through subtractive milling, where the film exhibited improved conduction properties after removal of surface layers, are investigated. Using statistical analysis and finite element modeling, we inferred that the mechanism of tunable microwave conductance is the variable area of the domain wall in the switching volume. These observations open the possibilities for ferroelectric memristors or volatile resistive switches, localized to several tens of nanometers and operating according to well‐defined dynamics under an applied field. 

Abstract Temperature‐ and electric‐field‐induced structural transitions in a polydomain ferroelectric can have profound effects on its electrothermal susceptibilities. Here, the role of such ferroelastic domains on the pyroelectric and electrocaloric response is experimentally investigated in thin films of the tetragonal ferroelectric PbZr_0.2Ti_0.8O₃. By utilizing epitaxial strain, a rich set of ferroelastic polydomain states spanning a broad thermodynamic phase space are stabilized. Using temperature‐dependent scanning‐probe microscopy, X‐ray diffraction, and high‐frequency phase‐sensitive pyroelectric measurements, the propensity of domains to reconfigure under a temperature perturbation is quantitatively studied. In turn, the “extrinsic” contributions to pyroelectricity exclusively due to changes between the ferroelastic domain population is elucidated as a function of epitaxial strain. Further, using highly sensitive thin‐film resistive thermometry, direct electrocaloric temperature changes are measured on these polydomain thin films for the first time. The results demonstrate that temperature‐ and electric‐field‐driven domain interconversion under compressive strain diminish both the pyroelectric and the electrocaloric effects, while both these susceptibilities are enhanced due to the exact‐opposite effect from the extrinsic contributions under tensile strain. 

Abstract Many energy conversion, sensing, and microelectronic applications based on ferroic materials are determined by the domain structure evolution under applied stimuli. New hyperspectral, multidimensional spectroscopic techniques now probe dynamic responses at relevant length and time scales to provide an understanding of how these nanoscale domain structures impact macroscopic properties. Such approaches, however, remain limited in use because of the difficulties that exist in extracting and visualizing scientific insights from these complex datasets. Using multidimensional band‐excitation scanning probe spectroscopy and adapting tools from both computer vision and machine learning, an automated workflow is developed to featurize, detect, and classify signatures of ferroelectric/ferroelastic switching processes in complex ferroelectric domain structures. This approach enables the identification and nanoscale visualization of varied modes of response and a pathway to statistically meaningful quantification of the differences between those modes. Among other things, the importance of domain geometry is spatially visualized for enhancing nanoscale electromechanical energy conversion.