Local Probe Comparison of Ferroelectric Switching Event Statistics in the Creep and Depinning Regimes in Pb(Zr_{0.2}Ti_{0.8})O_{3} Thin Films.

Ferroelectric materials provide a useful model system to explore the jerky, highly nonlinear dynamics of elastic interfaces in disordered media. The distribution of nanoscale switching event sizes is studied in two Pb(Zr_{0.2}Ti_{0.8})O_{3} thin films with different disorder landscapes using piezoresponse force microscopy. While the switching event statistics show the expected power-law scaling, significant variations in the value of the scaling exponent τ are seen, possibly as a consequence of the different intrinsic disorder landscapes in the samples and of further alterations under high tip bias applied during domain writing. Importantly, higher exponent values (1.98-2.87) are observed when crackling statistics are acquired only for events occurring in the creep regime. The exponents are systematically lowered when all events across both creep and depinning regimes are considered-the first time such a distinction is made in studies of ferroelectric materials. These results show that distinguishing the two regimes is of crucial importance, significantly affecting the exponent value and potentially leading to incorrect assignment of universality class.

Chemical Phenomena of Atomic Force Microscopy Scanning.

Atomic force microscopy is widely used for nanoscale characterization of materials by scientists worldwide. The long-held belief of ambient AFM is that the tip is generally chemically inert but can be functionalized with respect to the studied sample. This implies that basic imaging and scanning procedures do not affect surface and bulk chemistry of the studied sample. However, an in-depth study of the confined chemical processes taking place at the tip-surface junction and the associated chemical changes to the material surface have been missing as of now. Here, we used a hybrid system that combines time-of-flight secondary ion mass spectrometry with an atomic force microscopy to investigate the chemical interactions that take place at the tip-surface junction. Investigations showed that even basic contact mode AFM scanning is able to modify the surface of the studied sample. In particular, we found that the silicone oils deposited from the AFM tip into the scanned regions and spread to distances exceeding 15 µm from the tip. These oils were determined to come from standard gel boxes used for the storage of the tips. The explored phenomena are important for interpreting and understanding results of AFM mechanical and electrical studies relying on the state of the tip-surface junction.

Subtractive fabrication of ferroelectric thin films with precisely controlled thickness.

The ability to control thin-film growth has led to advances in our understanding of fundamental physics as well as to the emergence of novel technologies. However, common thin-film growth techniques introduce a number of limitations related to the concentration of defects on film interfaces and surfaces that limit the scope of systems that can be produced and studied experimentally. Here, we developed an ion-beam based subtractive fabrication process that enables creation and modification of thin films with pre-defined thicknesses. To accomplish this we transformed a multimodal imaging platform that combines time-of-flight secondary ion mass spectrometry with atomic force microscopy to a unique fabrication tool that allows for precise sputtering of the nanometer-thin layers of material. To demonstrate fabrication of thin-films with in situ feedback and control on film thickness and functionality we systematically studied thickness dependence of ferroelectric switching of lead-zirconate-titanate, within a single epitaxial film. Our results demonstrate that through a subtractive film fabrication process we can control the piezoelectric response as a function of film thickness as well as improve on the overall piezoelectric response versus an untreated film.

The Effect of Chemical Environment and Temperature on the Domain Structure of Free-Standing BaTiO3 via In Situ STEM.

Ferroelectrics, due to their polar nature and reversible switching, can be used to dynamically control surface chemistry for catalysis, chemical switching, and other applications such as water splitting. However, this is a complex phenomenon where ferroelectric domain orientation and switching are intimately linked to surface charges. In this work, the temperature-induced domain behavior of ferroelectric-ferroelastic domains in free-standing BaTiO3 films under different gas environments, including vacuum and oxygen-rich, is studied by in situ scanning transmission electron microscopy (STEM). An automated pathway to statistically disentangle and detect domain structure transformations using deep autoencoders, providing a pathway towards real-time analysis is also established. These results show a clear difference in the temperature at which phase transition occurs and the domain behavior between various environments, with a peculiar domain reconfiguration at low temperatures, from a-c to a-a at ≈60 °C. The vacuum environment exhibits a rich domain structure, while under the oxidizing environment, the domain structure is largely suppressed. The direct visualization provided by in situ gas and heating STEM allows to investigate the influence of external variables such as gas, pressure, and temperature, on oxide surfaces in a dynamic manner, providing invaluable insights into the intricate surface-screening mechanisms in ferroelectrics.

Corrigendum: Applications and techniques for fast machine learning in science.

[This corrects the article DOI: 10.3389/fdata.2022.787421.].

Why it is Unfortunate that Linear Machine Learning "Works" so well in Electromechanical Switching of Ferroelectric Thin Films.

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) ß-variational autoencoders (ß-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 ß 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.

High Velocity, Low-Voltage Collective In-Plane Switching in (100) BaTiO3 Thin Films.

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) BaTiO3 films 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 1 ¯ $\bar{1}$ ] 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-1 at 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.

Applications and Techniques for Fast Machine Learning in Science.

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science-the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.

Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration.

Multiscale and multimodal imaging of material structures and properties provides solid ground on which materials theory and design can flourish. Recently, KAIST announced 10 flagship research fields, which include KAIST Materials Revolution: Materials and Molecular Modeling, Imaging, Informatics and Integration (M3I3). The M3I3 initiative aims to reduce the time for the discovery, design and development of materials based on elucidating multiscale processing-structure-property relationship and materials hierarchy, which are to be quantified and understood through a combination of machine learning and scientific insights. In this review, we begin by introducing recent progress on related initiatives around the globe, such as the Materials Genome Initiative (U.S.), Materials Informatics (U.S.), the Materials Project (U.S.), the Open Quantum Materials Database (U.S.), Materials Research by Information Integration Initiative (Japan), Novel Materials Discovery (E.U.), the NOMAD repository (E.U.), Materials Scientific Data Sharing Network (China), Vom Materials Zur Innovation (Germany), and Creative Materials Discovery (Korea), and discuss the role of multiscale materials and molecular imaging combined with machine learning in realizing the vision of M3I3. Specifically, microscopies using photons, electrons, and physical probes will be revisited with a focus on the multiscale structural hierarchy, as well as structure-property relationships. Additionally, data mining from the literature combined with machine learning will be shown to be more efficient in finding the future direction of materials structures with improved properties than the classical approach. Examples of materials for applications in energy and information will be reviewed and discussed. A case study on the development of a Ni-Co-Mn cathode materials illustrates M3I3's approach to creating libraries of multiscale structure-property-processing relationships. We end with a future outlook toward recent developments in the field of M3I3.

Understanding the Role of Ferroelastic Domains on the Pyroelectric and Electrocaloric Effects in Ferroelectric Thin Films.

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 PbZr0.2 Ti0.8 O3 . 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.

Revealing ferroelectric switching character using deep recurrent neural networks.

The ability to manipulate domains underpins function in applications of ferroelectrics. While there have been demonstrations of controlled nanoscale manipulation of domain structures to drive emergent properties, such approaches lack an internal feedback loop required for automatic manipulation. Here, using a deep sequence-to-sequence autoencoder we automate the extraction of latent features of nanoscale ferroelectric switching from piezoresponse force spectroscopy of tensile-strained PbZr0.2Ti0.8O3 with a hierarchical domain structure. We identify characteristic behavior in the piezoresponse and cantilever resonance hysteresis loops, which allows for the classification and quantification of nanoscale-switching mechanisms. Specifically, we identify elastic hardening events which are associated with the nucleation and growth of charged domain walls. This work demonstrates the efficacy of unsupervised neural networks in learning features of a material's physical response from nanoscale multichannel hyperspectral imagery and provides new capabilities in leveraging in operando spectroscopies that could enable the automated manipulation of nanoscale structures in materials.

Nanoscale Electrochemical Phenomena of Polarization Switching in Ferroelectrics.

Polarization switching is a fundamental feature of ferroelectric materials, enabling a plethora of applications and captivating the attention of the scientific community for over half a century. Many previous studies considered ferroelectric switching as a purely physical process, whereas polarization is fully controlled by the superposition of electric fields. However, screening charge is required for thermodynamic stability of the single domain state that is of interest in many technological applications. The screening process has always been assumed to be fast; thus, the rate-limiting phenomena were believed to be domain nucleation and domain wall dynamics. In this manuscript, we demonstrate that polarization switching under an atomic force microscopy tip leads to reversible ionic motion in the top 3 nm of PbZr0.2Ti0.8O3 surface layer. This evidence points to a strong chemical component to a process believed to be purely physical and has major implications for understanding ferroelectric materials, making ferroelectric devices, and interpreting local ferroelectric switching.

Machine Detection of Enhanced Electromechanical Energy Conversion in PbZr0.2 Ti0.8 O3 Thin Films.

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.

Strain-induced growth instability and nanoscale surface patterning in perovskite thin films.

Despite extensive studies on the effects of epitaxial strain on the evolution of the lattice and properties of materials, considerably less work has explored the impact of strain on growth dynamics. In this work, we demonstrate a growth-mode transition from 2D-step flow to self-organized, nanoscale 3D-island formation in PbZr0.2Ti0.8O3/SrRuO3/SrTiO3 (001) heterostructures as the kinetics of the growth process respond to the evolution of strain. With increasing heterostructure thickness and misfit dislocation formation at the buried interface, a periodic, modulated strain field is generated that alters the adatom binding energy and, in turn, leads to a kinetic instability that drives a transition from 2D growth to ordered, 3D-island formation. The results suggest that the periodically varying binding energy can lead to inhomogeneous adsorption kinetics causing preferential growth at certain sites. This, in conjunction with the presence of an Ehrlich-Schwoebel barrier, gives rise to long-range, periodically-ordered arrays of so-called "wedding cake" 3D nanostructures which self-assemble along the [100] and [010].

New modalities of strain-control of ferroelectric thin films.

Ferroelectrics, with their spontaneous switchable electric polarization and strong coupling between their electrical, mechanical, thermal, and optical responses, provide functionalities crucial for a diverse range of applications. Over the past decade, there has been significant progress in epitaxial strain engineering of oxide ferroelectric thin films to control and enhance the nature of ferroelectric order, alter ferroelectric susceptibilities, and to create new modes of response which can be harnessed for various applications. This review aims to cover some of the most important discoveries in strain engineering over the past decade and highlight some of the new and emerging approaches for strain control of ferroelectrics. We discuss how these new approaches to strain engineering provide promising routes to control and decouple ferroelectric susceptibilities and create new modes of response not possible in the confines of conventional strain engineering. To conclude, we will provide an overview and prospectus of these new and interesting modalities of strain engineering helping to accelerate their widespread development and implementation in future functional devices.

Complex Evolution of Built-in Potential in Compositionally-Graded PbZr(1-x)Ti(x)O3 Thin Films.

Epitaxial strain has been widely used to tune crystal and domain structures in ferroelectric thin films. New avenues of strain engineering based on varying the composition at the nanometer scale have been shown to generate symmetry breaking and large strain gradients culminating in large built-in potentials. In this work, we develop routes to deterministically control these built-in potentials by exploiting the interplay between strain gradients, strain accommodation, and domain formation in compositionally graded PbZr1-xTixO3 heterostructures. We demonstrate that variations in the nature of the compositional gradient and heterostructure thickness can be used to control both the crystal and domain structures and give rise to nonintuitive evolution of the built-in potential, which does not scale directly with the magnitude of the strain gradient as would be expected. Instead, large built-in potentials are observed in compositionally-graded heterostructures that contain (1) compositional gradients that traverse chemistries associated with structural phase boundaries (such as the morphotropic phase boundary) and (2) ferroelastic domain structures. In turn, the built-in potential is observed to be dependent on a combination of flexoelectric effects (i.e., polarization-strain gradient coupling), chemical-gradient effects (i.e., polarization-chemical potential gradient coupling), and local inhomogeneities (in structure or chemistry) that enhance strain (and/or chemical potential) gradients such as areas with nonlinear lattice parameter variation with chemistry or near ferroelastic domain boundaries. Regardless of origin, large built-in potentials act to suppress the dielectric permittivity, while having minimal impact on the magnitude of the polarization, which is important for the optimization of these materials for a range of nanoapplications from vibrational energy harvesting to thermal energy conversion and beyond.

Unexpected crystal and domain structures and properties in compositionally graded PbZr(1-x)Ti(x)O3 thin films.

Synthesis of compositionally graded versions of PbZr(1-x)Ti(x)O3 thin films results in unprecedented strains (as large as ≈4.5 × 10(5) m(-1)) and correspondingly unexpected crystal structures, ferroelectric domain structures, and properties. This includes the observation of built-in electric fields in films as large as 200 kV/cm. Compositional and strain gradients could represent a new direction of strain-control of materials.