Relaxor ferroelectrics (RFEs) exhibit ultra-high piezoresponse and unique frequency-dependent dielectric properties making them an attractive choice for next-generation electronic devices. However, the underlying mechanisms governing the structural evolution and dynamic behavior of polar nanodomains (PNDs) under an applied electric field remain a significant open question. Here, time-resolved X-ray microdiffraction technique with picosecond resolution is utilized to investigate the structural dynamics of PNDs in (111)-oriented Sn-doped BaTiO3 (BTS) epitaxial RFE thin films. The findings demonstrate that the polarization of nanodomains rotates towards the out-of-plane direction, leading to modulation of d-spacing values as well as contributing to an ultrahigh piezoelectric response. An anomalous polarity dependence in piezoelectric strain is observed, with a higher strain under a negative electric field. The strain response also shows a strong dependence on the pulse width/ frequency of the electric field, along with an ultra-high piezoelectric strain of up to 1.2%, outperforming various Pb-based relaxor systems. Our findings reveal an intricate interplay between polarization rotation dynamics and electric field polarity in RFEs. These insights not only redefine our understanding of PND dynamics but also pave the way for the development of sustainable, high-performance Pb-free piezoelectrics, ultra-high energy density capacitors, nanoactuators and ultra-compact electronic devices.

Alzheimer's disease (AD) is the most frequent neurodegenerative disorder in the elderly aged over 65. The extracellular accumulation of beta-amyloid (Aβ) aggregates in the brain is considered as the major event worsening the AD symptoms, but its underlying reason has remained unclear. Here the piezoelectric characteristics of Aβ aggregates are revealed. The vector piezoresponse force microscopy (PFM) analysis results exhibit that Aβ fibrils have spiraling piezoelectric domains along the length and a lateral piezoelectric constant of 44.1 pC N-1. Also, the continuous sideband Kelvin probe force microscopy (KPFM) images display that the increment of charge-induced surface potential on a single Aβ fibril is allowed to reach above +1700 mV in response to applied forces. These findings shed light on the peculiar mechano-electrical surface properties of pathological Aβ fibrils that exceed those of normal body components.

Switchable tribological properties of ferroelectrics offer an alternative route to visualize and control ferroelectric domains. Here, we observe the switchable friction and wear behavior of ferroelectrics using a nanoscale scanning probe—down domains have lower friction coefficients and show slower wear rates than up domains and can be used as smart masks. This asymmetry is enabled by flexoelectrically coupled polarization in the up and down domains under a sufficiently high contact force. Moreover, we determine that this polarization-sensitive tribological asymmetry is widely applicable across various ferroelectrics with different chemical compositions and crystalline symmetry. Finally, using this switchable tribology and multi-pass patterning with a domain-based dynamic smart mask, we demonstrate three-dimensional nanostructuring exploiting the asymmetric wear rates of up and down domains, which can, furthermore, be scaled up to technologically relevant (mm–cm) size. These findings demonstrate that ferroelectrics are electrically tunable tribological materials at the nanoscale for versatile applications.

Here, we present an EfficientNet-B0-based model to directly predict multiple properties of lithium manganese nickel oxides (LMNO) using their crystal structure images. The model is supposed to predict the energy above the convex hull, bandgap energy, crystal systems, and crystal space groups of LMNOs. In the last layer of the model, a linear function is used to predict the bandgap energy and energy above the convex hull, while a SoftMax function is used to classify the crystal systems and crystal space groups. In the test set, the percentages of coefficient of determination (R2) scores are 97.73 for the bandgap energy and energy above the convex hull predictions, respectively, while the percentages of accuracy are 99.45 crystal system and crystal space group classifications, respectively. The class saliency maps explain that the model pays more attention to the shape of the crystal lattices and gradients around the lattice region occupied by the larger ions. This work provides new insight into using an intelligent model to directly relate the crystal structures of LMNO materials with their properties.

High-throughput materials research is strongly required to accelerate the development of safe and high energy-density lithium-ion battery (LIB) applicable to electric vehicle and energy storage system. The artificial intelligence, including machine learning with neural networks such as Boltzmann neural networks and convolutional neural networks (CNN), is a powerful tool to explore next-generation electrode materials and functional additives. In this paper, we develop a prediction model that classifies the major composition (e.g., 333, 523, 622, and 811) and different states (e.g., pristine, pre-cycled, and 100 times cycled) of various Li(Ni, Co, Mn)O2 (NCM) cathodes via CNN trained on scanning electron microscopy (SEM) images. Based on those results, our trained CNN model shows a high accuracy of 99.6% where the number of test set is 3840. In addition, the model can be applied to the case of untrained SEM data of NCM cathodes with functional electrolyte additives.

Dendritic microstructures are ubiquitous in nature and are the primary solidification morphologies in metallic materials. Techniques such as X-ray computed tomography (XCT) have provided new insights into dendritic phase transformation phenomena. However, manual identification of dendritic morphologies in microscopy data can be both labor intensive and potentially ambiguous. The analysis of 3D datasets is particularly challenging due to their large sizes (terabytes) and the presence of artifacts scattered within the imaged volumes. In this study, we trained 3D convolutional neural networks (CNNs) to segment 3D datasets. Three CNN architectures were investigated, including a new version of FCDenseNet which we extended to 3D. We show that using hyperparameter optimization (HPO) and fine-tuning techniques, both 2D and 3D CNN architectures outperform the previous state of the art. The 3D U-Net architecture trained in this study produced the best segmentations according to quantitative metrics (intersection-over-union of 95.56% and a boundary displacement error of 0.58 pixels), while 3D FCDense produced the smoothest boundaries and best segmentations according to visual inspection. The trained 3D CNNs are able to segment entire 852 x 852 x 250 voxel 3D volumes in only -60 s, thus hastening the progress towards a deeper understanding of phase transformation phenomena such as dendritic solidification.

Piezoresponse force microscopy (PFM) has been widely used for the nanoscale analysis of piezoelectric properties and ferroelectric domains. Although PFM is useful because of its simple and nondestructive features, PFM measurements can be obscured by non-piezoelectric effects that could affect the PFM signals or lead to ferroelectric-like behaviors in non-ferroelectric materials. Many research studies have addressed related technical issues, but they have primarily focused on vertical PFM. Here, we investigate significant discrepancies in lateral PFM signals between the trace and the retrace scans, which are proportional to the scan angle and the cantilever lateral tilting discrepancy. The discrepancies in PFM signals are analyzed based on intrinsic and extrinsic components, including out-of-plane piezoresponse, electrostatic force, and other factors. Our research will contribute to the accurate PFM measurements for the visualization of ferroelectric in-plane polarization distributions.

Abstract Machine intelligence has garnered immense attention owing to its ability to discover hidden patterns in abstract and high-dimensional datasets. However, its success is often limited by the fundamental bottleneck of data scarcity. In this work, we offer a universal data augmentation solution to resolve this impasse. We first discovered the hidden knowledge within the existing scarce dataset using the machine learning (ML) technique and then synthetically augmented the dataset according to its feature importance. In principle, scarce and augmented datasets should share a common statistical property. Using this property, we specifically study the scarce dataset representing the binge-drinking behavior of university students and show that our method is effective in augmenting a limited dataset with high fidelity. The current work challenges the status quo in data scarcity with rule-less-based ML, which removes the ostensible barrier that prevents the application of data-driven techniques to the data scarce clinical research.

Si/Y-HfO2/Pt shows a large electrocaloric temperature change (ΔT) of 24.84 K with high reliability on thermal and electrical field cycling.

Optimizing synthesis parameters is crucial in fabricating an ideal cathode material; however, the design space is too vast to be fully explored using an Edisonian approach. Here, by clustering eleven domain-expert-deriveddescriptors from literature, we use an inverse design surrogate model to build up the experimental parameters-property relationship. Without struggling with the trial-and-error method, the model enables design variables prediction that serves as an effective strategy for cathode retrosynthesis. More importantly, not only did we overcome the data scarcity problem, but the machine learning model has guided us to achieve cathode with high discharge capacity and Coulombic efficiency of 209.5 mAh/g and 86%, respectively. This work demonstrates an inverse design-to-device pipeline with unprecedented potential to accelerate the discovery of highenergy-density cathodes.

Dendritic microstructures are ubiquitous in nature and are the primary solidification morphologies in metallic materials. Techniques such as x-ray computed tomography (XCT) have provided new insights into dendritic phase transformation phenomena. However, manual identification of dendritic morphologies in microscopy data can be both labor intensive and potentially ambiguous. The analysis of 3D datasets is particularly challenging due to their large sizes (terabytes) and the presence of artifacts scattered within the imaged volumes. In this study, we trained 3D convolutional neural networks (CNNs) to segment 3D datasets. Three CNN architectures were investigated, including a new 3D version of FCDense. We show that using hyperparameter optimization (HPO) and fine-tuning techniques, both 2D and 3D CNN architectures can be trained to outperform the previous state of the art. The 3D U-Net architecture trained in this study produced the best segmentations according to quantitative metrics (pixel-wise accuracy of 99.84% and a boundary displacement error of 0.58 pixels), while 3D FCDense produced the smoothest boundaries and best segmentations according to visual inspection. The trained 3D CNNs are able to segment entire 852 x 852 x 250 voxel 3D volumes in only ~60 seconds, thus hastening the progress towards a deeper understanding of phase transformation phenomena such as dendritic solidification.

Low-temperature deposition of inorganic ferroelectric (FE) thin films is highly demanded for lowering the environmental impact through lesser energy consumption. Doped HfO2-based FE thin films commonly require high crystalline temperature (>450 degrees C) to exhibit ferroelectricity. Here, we report that the crystallization temperature of the metastable orthorhombic (O) phase in Hf0.5Zr0.5O2 thin films can be lowered to 350 degrees C via a deep ultraviolet (DUV) irradiation process carried out before rapid thermal annealing (RTA). The DUV irradiation initializes a nucleation process of the crystal growth through photochemical cleavage of organic residues, followed by the densification of metal oxide films, and the subsequent RTA at 350 degrees C crystallizes the Hf0.5Zr0.5O2 thin film with a higher O-phase fraction. The DUV-irradiated films annealed at 350 degrees C exhibited FE characteristics, whereas the non-irradiated films annealed above 450 degrees C become FE. Our study suggests that DUV irradiation can be successfully utilized to lower the crystallization temperature of HZO thin films and can lead to the realization of flexible FE random access memories.

The ability to quickly analyze large imaging datasets is vital to the widespread adoption of modern materials characterization tools, and thus the development of new materials. Image segmentation can be the most subjective and time-consuming step in the data analysis workflow. A promising approach to segmentation of large materials datasets is the use of convolutional neural networks (CNNs). However, a major challenge is to obtain the images and segmentations needed for CNN training, since this requires segmentations performed by humans. We show that it is possible to segment experimental materials science data using a SegNet-based CNN that was trained only using simple phase field simulations. A test image from an in-situ solidification experiment of an Al-Zn alloy was used to parameterize the phase field simulations. The most important microstructural features required for the best CNN to “understand” the contents of the image are ranked as: (1) having training images with diffuse particle-background interfaces, (2) modifying the images by adding noise, (3) removing particles at the image edges, and (4) adding sub-images to the particles to account for the feint bands present on some dendrites. The CNN trained on phase field images segmented the experimental test image with 99.3% accuracy, comparable to CNNs trained on experimental data. This approach of using computationally generated images to train CNNs capable of segmenting experiments will accelerate the rate of materials design and discovery.

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.

Ferroelectricity in HfO 2 thin films can be utilized for fast, power‐efficient, and highly scalable non‐volatile memories. However, the required wake‐up process for inducing ferroelectricity/ achieving higher polarization is one of the major hurdles that hinder HfO 2 ‐based thin films from developing reliable electronic devices. The wake‐up effect is believed to originate from i) phase transformation from non‐ferroelectric to ferroelectric, ii) movement of defect entities (mainly oxygen vacancy defects) near the film‐electrode interface, and iii) heterogeneity of the electrode interfaces. In the present study, an experimental strategy is designed to overcome these sources of the wake‐up process. A multi‐step deposition and annealing process is carried out to induce wake‐up‐free ferroelectricity in Yttrium doped HfO 2 (Y:HfO 2 ) thin film directly grown on Si‐substrate. Furnace annealing is utilized instead of the standard rapid thermal annealing process to reduce the oxygen deficiencies and stimulate the direct growth of the polar Y:HfO 2 . The oxygen‐vacancy‐related defects are found to be the dominating source of wake‐up effect in Y‐doped HfO 2 films. The step‐wise deposition and annealing in the oxygen atmosphere facilitate direct growth of the polar phase, reduce the oxygen vacancies, and induce wake‐up‐free ferroelectricity in Y:HfO 2 .

Machine intelligence-based optimizing synthesis parameters offers exciting opportunities in various domain of materials chemistry where large and comprehensive dataset are available. Aiming to construct a “design-to-device” pipeline for battery application with small discrepancy, we adapt machine learning model based on actual experimental data from curated literatures to predict the discharge capacity of Nickel-Manganese-Cobalt (NCM) cathode material. However, missing data is an inherent problem from the results based on literature. Herein we introduce imputation techniques to fill the missing data based. To this end, we show that our model have remarkable predictive capability with the average coefficient of determinant, R2 = 0.84 for training sets. In addition, we also validate our model prediction by the experiments, which yields in less than 8% error. The present work highlights a promising data driven materials synthesis approach, which underpins nonlinear structure-properties relationship for broad range of materials development.

Dental erosion has become a prevalence disease and attracted increasing attention worldwide. In this research, we quantitatively evaluate the mechanical and morphological changes in the very early stages of softening and weakening of human enamel surfaces induced by soft drinks using atomic force microscopy (AFM). With an increase of the immersion time in soft drinks, we found a significant increase of surface roughness (Rq) of the enamel surface. The prismatic structure of enamel was clearly observed after a 1-h immersion in Coca-Cola®, which shows its strong erosion effect. According to the elastic modulus mapping images obtained by AFM, a considerable decrease of elastic modulus (E) of enamel surface has been found as the enamel surface structures are etched away by soft drinks. A high surface roughness of enamel will result in a high chance of cavities due to easier bacterial adhesion on rougher surface, while a drastic deterioration of the mechanical properties of the enamel will weaken its protection property. Our findings show the serious influence of acidic drinks on enamel surface at the very beginning stage of etching process, which is quite meaningful for people to prevent dental erosion and keep dental health.

Here we quantitatively evaluate the early stages of mechanical and morphological changes of polished human enamel surfaces induced by soft drinks using atomic force microscopy. With an increase of the immersion time in soft drinks, we found a significant increase of surface roughness (Ra) and a considerable decrease of elastic modulus (E) of the enamel. The prismatic structure of enamel was clearly observed after a one-hour immersion in Coca-Cola, which shows its strong erosion effect. A high surface roughness of enamel results in a high chance of cavities due to easier bacterial adhesion on rougher surface, while a drastic deterioration of the mechanical properties of the enamel weakens its protection property. Our findings show the variation of enamel surface at the very beginning stage of etching process by acidic drinks, which can also be applicable to the etching mechanism of enamel surface by other sources.