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Abstract Piezoelectric fiber yarns produced by electrospinning offer a versatile platform for intelligent devices, demonstrating mechanical durability and the ability to convert mechanical strain into electric signals. While conventional methods involve twisting a single poly(vinylidene fluoride‐co‐trifluoroethylene)(P(VDF‐TrFE)) fiber mat to create yarns, by limiting control over the mechanical properties, an approach inspired by composite laminate design principles is proposed for strengthening. By stacking multiple electrospun mats in various sequences and twisting them into yarns, the mechanical properties of P(VDF‐TrFE) yarn structures are efficiently optimized. By leveraging a multi‐objective Bayesian optimization‐based machine learning algorithm without imposing specific stacking restrictions, an optimal stacking sequence is determined that simultaneously enhances the ultimate tensile strength (UTS) and failure strain by considering the orientation angles of each aligned fiber mat as discrete design variables. The conditions on the Pareto front that achieve a balanced improvement in both the UTS and failure strain are identified. Additionally, applying corona poling induces extra dipole polarization in the yarn state, successfully fabricating mechanically robust and high‐performance piezoelectric P(VDF‐TrFE) yarns. Ultimately, the mechanically strengthened piezoelectric yarns demonstrate superior capabilities in self‐powered sensing applications, particularly in challenging environments and sports scenarios, substantiating their potential for real‐time signal detection.

Recently, grid composites have drawn considerable attention in various engineering applications due to their customizable mechanical properties, stemming from a wide spectrum of available configurations. Advanced additive manufacturing techniques have made the production of these configurations more feasible. While much research has focused on optimizing for stiffness and strength, there has been a relative lack of studies targeting toughness optimization, mainly due to the intricate unpredictability arising from the complexity of crack propagation mechanisms. This study seeks to fill that gap by leveraging a hierarchical deep neural network (DNN) model, integrated with finite element method (FEM) simulations, to maximize grid composite toughness. DNN model incorporates both structural configuration and stress field data, improving prediction accuracy for unseen domains. Using a genetic algorithm informed by the DNN model, a substantial 60% increase in toughness was achieved while investigating only 3.5% of the original dataset. Subsequent analyses of the stress field and crack phase field elucidated the mechanisms contributing to this increased toughness. Finally, the optimized configuration was experimentally validated by fabricating specimens using a polyjet 3D printer. The results represent a significant advancement in maximizing energy absorption, offering a substantial contribution to the field of composite material optimization.

Abstract Piezoelectric polymer fibers offer a fundamental element in intelligent fabrics with their shape adaptability and energy‐conversion capability for wearable activity and health monitoring applications. Nonetheless, realizing high‐performance smart polymer fibers faces a technical challenge due to the relatively low piezoelectric performance. Here, we demonstrate high‐performance piezoelectric yarns simultaneously equipped with structural robustness and mechanical flexibility. The key to substantially enhanced piezoelectric performance is promoting the electroactive β‐phase formation during electrospinning via adding an adequate amount of barium titanate (BaTiO3) nanoparticles into the poly(vinylidene fluoride‐trifluoroethylene) (P(VDF‐TrFE)). When transformed into a yarn structure by twisting the electrospun mats, the BaTiO3‐doped P(VDF‐TrFE) fibers become mechanically strengthened with significantly improved elastic modulus and ductility. Owing to the tailored convolution neural network algorithms architected for classification, the as‐developed BaTiO3‐doped piezo‐yarn device woven into a cotton fabric exhibits monitoring and identifying capabilities for body signals during seven human motion activities with a high accuracy of 99.6%.

Because the properties of thermoelectric (TE) materials are strongly dependent on temperature and differ considerably, segmented TE legs composed of multiple stacked TE materials have been investigated to provide efficient operation of TE devices. However, owing to the inherent nonlinearity that limits the application of conventional optimization approaches, the optimal configuration of segmented TEGs (STEGs) must have been sought heuristically. In this study, we propose a systematic approach that enables the efficient exploration and exploitation of the vast design space of STEGs by leveraging the fast inference of deep learning. First, we train a neural network (NN) model using a dataset generated from finite element analysis (FEA) for a single TE leg with four stacked segments from among 18 TE materials with varying segment lengths and external loads. We then use a genetic optimization algorithm (GOA) with our trained NN model to search for high-performance design candidates. The performance of the new candidates is computed and validated based on FEA, and the results are used to update the NN through an active learning technique. After iteratively performing the procedure, we can fine-tune the TE legs for optimal efficiency, optimal power, and given combinations of both power and efficiency. Furthermore, we discuss the physical origin of optimally performing STEGs.

Abstract Structural health monitoring (SHM) techniques often require a large number of sensors to evaluate and monitor the structural health. In this paper, we propose a deep neural network (DNN)-based SHM method for accurate crack detection and localization in real time using a small number of strain gauge sensors and confirm its feasibility based on experimental data. The proposed method combines a DNN model with principal component analysis (PCA) to predict the strain field based on the local strains measured by strain gauge sensors located rather sparsely. We demonstrate the potential of the proposed technique via a cyclic 4-point bending test performed on a composite material specimen without cracks and seven specimens with different lengths of cracks. A dataset containing local strains measured with 12 strain gauge sensors and strain field measured with a digital image correlation (DIC) device was prepared. The strain field dataset from DIC is converted to a smaller dimension latent space with a few eigen basis via PCA, and a DNN model is trained to predict principal component values of each image with 12 strain gauge sensor measurements as input. The proposed method turns out to accurately predict the strain field for all specimens considered in the study.

Understanding 3D interfacial strain at the atomic level has been a long-sought challenge in the field of core-shell nanomaterials. Here, the authors address this challenge by revealing the full 3D atomic structures of Pd@Pt core-shell nanoparticles

Significant attention has been paid to developing highly flexible and highly stretchable strain sensors due to the increasing demand for wearable devices such as motion-capturing devices and health-monitoring devices. Especially, carbon nanotube (CNT) network-based elastomeric sensors have been studied extensively for their unique strong piezoresistive response under large deformation. Despite its importance for the facile design of sensors, the effect of length and volume fraction of CNT on the piezoresistivity over a large strain range has not been fully uncovered. In this study, by combining coarse-grained molecular statics (CGMS) simulations and efficient percolation network analysis, we investigate the piezoresistive response of the CNT network for a wide range of the length and volume fraction and visualized the CNT network topology to understand the mechanism behind the piezoresistivity response. Based on the set of calculations, we obtain the design map of stretchability and sensitivity for the CNT-elastomer nanocomposite sensors over a wide range of design parameters of CNT, which can be used to fabricate the strain sensor with a desired performance.

Medical concentric tubes have recently been designed using auxetic structures with negative Poisson's ratios to reduce undesirable snapping instability and improve mechanical performance. Despite the vast range of stiffness levels, prior auxetic designs were restricted to relatively simple geometries like square or triangular holes. Here, we present a novel free-form optimization for designing the bending and torsional stiffness of tubular structures using the multi-objective Bayesian optimization (MBO) method. The initial dataset of hole shapes is generated using a non-parametric Bézier curve, and the corresponding mechanical properties are estimated via numerical analysis. The acquisition function suggests a new hole shape with higher performance metrics based on the dataset, and updates to the regression model are made with new data points to improve prediction accuracy. Compared to a conventional tube with rectangular holes, the optimized design improves key factor, a ratio of bending stiffness to torsional rigidity, and torsional stiffness by 32% and 25%, respectively, by expanding the existing design space. The performance of the optimally designed tube was validated by resonant vibration tests.

Recently, the design of grid composites with superior mechanical properties has gained significant attention as a testbed for deep neural network (DNN)-based optimization methods. However, current designed DNN architectures are not specifically tailored for grid composites and thus show weak generalizability in exploring unseen configurations that stem away from the training datasets. Here, a multiscale kernel neural network (MNet) is proposed that can efficiently predict the strain field within a grid composite subject to an external loading. Predicting the strain field of a composite is especially important when it comes to understanding how the material will behave under loading. MNet enables accurate predictions of the strain field for completely new configurations in unseen domain, with a reduced mean absolute percentage error (MAPE) by 50% compared to a benchmark, U-Net as current state-of-the arts DNN architectures. In addition, results showed that MNet maintained superb performances with less than one-third of dataset, and can be applied to grid composites larger than the composite configurations used for the initial training. By investigating the inference mechanisms from the kernels of multiple sizes, our work revealed that the MNet can efficiently extract various spatial correlations from the material distribution.

Abstract Neural network-based generative models have been actively investigated as an inverse design method for finding novel materials in a vast design space. However, the applicability of conventional generative models is limited because they cannot access data outside the range of training sets. Advanced generative models that were devised to overcome the limitation also suffer from the weak predictive power on the unseen domain. In this study, we propose a deep neural network-based forward design approach that enables an efficient search for superior materials far beyond the domain of the initial training set. This approach compensates for the weak predictive power of neural networks on an unseen domain through gradual updates of the neural network with active transfer learning and data augmentation methods. We demonstrate the potential of our framework with a grid composite optimization problem that has an astronomical number of possible design configurations. Results show that our proposed framework can provide excellent designs close to the global optima, even with the addition of a very small dataset corresponding to less than 0.5% of the initial training dataset size.

Plastic deformation of metallic glasses, which show no long-range structural order, proceeds by shear transformation of a local group of atoms referred to as the shear transformation zone (STZ). Unlike crystalline solids, it is difficult to identify STZs and predict the onset of plasticity from a random atomic configuration under a given loading. Recently, significant efforts have been made to predict the shear transformation with initial atomic properties using machine learning. However, despite the class imbalance, where the atoms participating in shear transformation is much rarer compared to the others, few studies have explored the issue of the proper predictive metric choice, with most studies considering widely used metrics such as Recall or AUC in the machine learning community. Therefore, here we train a graph neural network that predicts the initially activated STZ and evaluate its predictive power using various metrics considered to be proper for handling imbalanced datasets. We find that the AUC value is significantly overestimated due to the class imbalance and too many atoms are misclassified as initial STZ, so other metrics such as the precision, f1, MCC, and AP indicate very low predictive power close to zero. Additionally, we reveal that the predictive performance changes significantly over the threshold value of non-affine displacement, above which an atom is classified as the initially activated STZ, due to the change in the degree of class imbalance. Our study implies that it is crucial to use an identical threshold for this type of classification (i.e., the class ratio) for a fair assessment of ML models adapted in different studies and to holistically evaluate the predictive performance based on various metrics.

Abstract We studied the effects of important ternary elements, such as Cr, Nb, and V, on the plasticity of $$\upgamma $$ γ -TiAl crystals by calculating the point defect formation energy and the change in the generalized stacking fault energy (GSFE) surface from first-principles calculations. For all three elements, the point defect formation energies of the substitutional defects are lower in the Ti site than in the Al site, which implies that substitution on the Ti site is energetically more stable. We computed the GSFE surfaces with and without a substitutional solute and obtained the ideal critical resolved shear stress (ICRSS) of each partial slip. The change in the GSFE surface indicates that the substitution of Ti with Cr, Nb, or V results in an increase in the yield strength because the ICRSS of the superlattice intrinsic stacking fault (SISF) partial slip increases. Interestingly, we find that Cr substitution on an Al site could occur owing to the small difference between the substitutional defect formation energies of the Ti and Al sites. In that case, the reduction of ICRSSs of the SISF partial slip and twinning would lead to improved twinnability. We discuss the implications of the computational predictions by comparing them with experimental results in the literature.

Stress-strain curves are an important representation of a material's mechanical properties, from which important properties such as elastic modulus, strength, and toughness, are defined. However, generating stress-strain curves from numerical methods such as finite element method (FEM) is computationally intensive, especially when considering the entire failure path for a material. As a result, it is difficult to perform high throughput computational design of materials with large design spaces, especially when considering mechanical responses beyond the elastic limit. In this work, a combination of principal component analysis (PCA) and convolutional neural networks (CNN) are used to predict the entire stress-strain behavior of binary composites evaluated over the entire failure path, motivated by the significantly faster inference speed of empirical models. We show that PCA transforms the stress-strain curves into an effective latent space by visualizing the eigenbasis of PCA. Despite having a dataset of only 10-27% of possible microstructure configurations, the mean absolute error of the prediction is

An analytical method for predicting the velocity potential around a pulsating bubble close to a free or rigid wall was established using an image method. Because the velocity potential should satisfy two boundary conditions at the bubble surface and rigid wall, we investigated the velocity in the normal direction at the two boundaries by adding the image bubbles. The potential was analyzed by decomposing the bubble motion as two independent motions, pulsation and translation, and we found that when the number of image bubbles was greater than ten, the two boundary conditions were satisfied for the translation term. By adding many image bubbles after the approximation of the pulsation term, we also confirmed that the boundary condition at the wall was satisfied.

Abstract Interest in soft actuators for next-generation electronic devices, such as wearable electronics, haptic feedback systems, rollable flexible displays, and soft robotics, is rapidly growing. However, for more practical applications in diverse electronic devices, soft actuators require multiple functionalities including anisotropic actuation in three-dimensional space, active tactile feedback, and controllable wettability. Herein, we report anisotropic dielectric elastomer actuators with uni- and bi-axially wrinkled carbon black electrodes that are formed through pre-streching and relaxation processes. The wrinkled dielectric elastomer actuator (WDEA) that shows directional actuation under electric fields is used to control the anisotropic wettability. The morphology changes of the electrode surfaces under various electric stimuli are investigated by measuring the contact angles of water droplets, and the results show that the controllable wettability has a broad range from 141° to 161° along the wrinkle direction. The present study successfully demonstrates that the WDEA under electrically controlled inputs can be used to modulate the uni- or bi-axially wrinkled electrode surfaces with continous roughness levels. The controllable wrinkled structures can play an important role in creating adaptable water repellency and tunable anisotropic wettability.

Micromechanics-based homogenization has been employed extensively to predict the effective properties of technologically important composites. In this review article, we address its application to various physical phenomena, including elasticity, thermal and electrical conduction, electric, and magnetic polarization, as well as multi-physics phenomena governed by coupled equations such as piezoelectricity and thermoelectricity. Especially, for this special issue, we introduce several research works published recently from our research group that consider the anisotropy of the matrix and interfacial imperfections in obtaining various effective physical properties. We begin with a brief review of the concept of the Eshelby tensor with regard to the elasticity and mean-field homogenization of the effective stiffness tensor of a composite with a perfect interface between the matrix and inclusions. We then discuss the extension of the theory in two aspects. First, we discuss the mathematical analogy among steady-state equations describing the aforementioned physical phenomena and explain how the Eshelby tensor can be used to obtain various effective properties. Afterwards, we describe how the anisotropy of the matrix and interfacial imperfections, which exist in actual composites, can be accounted for. In the last section, we provide a summary and outlook considering future challenges.

The wettability properties of graphene hold promise for the realisation of flow control devices. Here, the authors demonstrate that the degree of water penetration through a nickel mesh coated with graphene can be controlled electrically, enabling dynamic locomotion of water droplets.

Possessing a strong piezoresistivity, nanocomposites of metal nanowires and elastomer have been studied extensively for its use in highly flexible, stretchable, and sensitive sensors. In this work, we analyze the working mechanism and performance of a nanocomposite based stretchable strain sensor by calculating the conductivity of the nanowire percolation network as a function of strain. We reveal that the nonlinear piezoresistivity is attributed to the topological change of percolation network, which leads to a bottleneck in the electric path. We find that, due to enhanced percolation, the linearity of the sensor improves with increasing aspect ratio or volume fraction of the nanowires at the expense of decreasing gauge factor. In addition, we show that a wide range of gauge factors (from negative to positive) can be obtained by changing the orientation distribution of nanowires. Our study suggests a way to intelligently design nanocomposite-based piezoresistive sensors for flexible and wearable devices.