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The organizational complexity of biominerals has long fascinated scientists seeking to understand biological programming and implement new developments in biomimetic materials chemistry. Nonclassical crystallization pathways have been observed and analyzed in typical crystalline biominerals, involving the controlled attachment and reconfiguration of nanoparticles and clusters on organic templates. However, the understanding of templated amorphous silica mineralization remains limited, hindering the rational design of complex silica-based materials. Here, we present a systematic study on the stabilization of self-capping cationic silica cluster (CSC) and their assembly dynamics using DNA nanostructures as programmable attachment templates. By tuning the composition and structure of CSC, we demonstrate high-fidelity silicification at single-cluster resolution, revealing a process of adaptive templating involving cooperative adjustments of both the DNA framework and cluster morphology. Our results provide a unified model of silicification by cluster attachment and pave the way towards the molecular tuning of pre- and post-nucleation stages of sol-gel reactions. Overall, our findings provide new insights for the design of silica-based materials with controlled organization and functionality, bridging the gap between biomineralization principles and the rational design of biomimetic material.

DNA monolayers with inherent chirality play a pivotal role across various domains, including biosensors, DNA chips, and bioelectronics. Nonetheless, conventional DNA chiral monolayers, typically constructed from single-stranded DNA (ssDNA) or double-stranded DNA (dsDNA), often lack structural orderliness and design flexibility at the interface. Structural DNA nanotechnology emerges as a promising solution to tackle these challenges. In this study, we present a strategy for crafting highly adaptable twisted DNA origami-based chiral monolayers. These structures exhibit distinct interfacial assembly characteristics and effectively mitigate the structural disorder of dsDNA monolayers, which is constrained by a limited persistence length of ~50 nm of dsDNA. We highlight the spin-filtering capabilities of four representative DNA origami-based chiral monolayers, demonstrating a maximal one-order-of-magnitude increase in spin-filtering efficiency per unit area compared to conventional dsDNA chiral monolayers. Intriguingly, our findings reveal that the higher-order, tertiary, chiral structure of twisted DNA origami further enhances the spin-filtering efficiency. This work paves the way for the rational design of DNA chiral monolayers.

Federated learning is a distributed machine learning system that uses participants' data to train an improved global model. In federated learning, participants cooperatively train a global model, and they will receive the global model and payments. Rational participants try to maximize their individual utility, and they will not input their high-quality data truthfully unless they are provided with satisfactory payments based on their data quality. Furthermore, federated learning benefits from the cooperative contributions of participants. Accordingly, how to establish an incentive mechanism that both incentivizes inputting data truthfully and promotes stable cooperation has become an important issue to consider. In this paper, we introduce a data sharing game model for federated learning and employ game-theoretic approaches to design a core-selecting incentive mechanism by utilizing a popular concept in cooperative games, the core. In federated learning, the core can be empty, resulting in the core-selecting mechanism becoming infeasible. To address this, our core-selecting mechanism employs a relaxation method and simultaneously minimizes the benefits of inputting false data for all participants. However, this mechanism is computationally expensive because it requires aggregating exponential models for all possible coalitions, which is infeasible in federated learning. To address this, we propose an efficient core-selecting mechanism based on sampling approximation that only aggregates models on sampled coalitions to approximate the exact result. Extensive experiments verify that the efficient core-selecting mechanism can incentivize inputting high-quality data and stable cooperation, while it reduces computational overhead compared to the core-selecting mechanism.

Solving Nash equilibrium is the key challenge in normal-form games with large strategy spaces, where open-ended learning frameworks offer an efficient approach. In this work, we propose an innovative unified open-ended learning framework A-PSRO, i.e., Advantage Policy Space Response Oracle, as a comprehensive framework for both zero-sum and general-sum games. In particular, we introduce the advantage function as an enhanced evaluation metric for strategies, enabling a unified learning objective for agents engaged in normal-form games. We prove that the advantage function exhibits favorable properties and is connected with the Nash equilibrium, which can be used as an objective to guide agents to learn strategies efficiently. Our experiments reveal that A-PSRO achieves a considerable decrease in exploitability in zero-sum games and an escalation in rewards in general-sum games, significantly outperforming previous PSRO algorithms.

Transformers-based methods have achieved significant performance in image deraining as they can model the non-local information which is vital for high-quality image reconstruction. In this paper, we find that most existing Transformers usually use all similarities of the tokens from the query-key pairs for the feature aggregation. However, if the tokens from the query are different from those of the key, the self-attention values estimated from these tokens also involve in feature aggregation, which accordingly interferes with the clear image restoration. To overcome this problem, we propose an effective DeRaining network, Sparse Transformer (DRSformer) that can adaptively keep the most useful self-attention values for feature aggregation so that the aggregated features better facilitate high-quality image reconstruction. Specifically, we develop a learnable top-k selection operator to adaptively retain the most crucial attention scores from the keys for each query for better feature aggregation. Simultaneously, as the naive feed-forward network in Transformers does not model the multi-scale information that is important for latent clear image restoration, we develop an effective mixed-scale feed-forward network to generate better features for image deraining. To learn an enriched set of hybrid features, which combines local context from CNN operators, we equip our model with mixture of experts feature compensator to present a cooperation refinement deraining scheme. Extensive experimental results on the commonly used benchmarks demonstrate that the proposed method achieves favorable performance against state-of-the-art approaches. The source code and trained models are available at https://github.com/cschenxiang/DRSformer., Comment: Accepted as a highlight paper in CVPR 2023

Dislocations, line defects in crystalline materials, play an essential role in the mechanical[1,2], electrical[3], optical[4], thermal[5], and phase transition[6] properties of these materials. Dislocation motion, an important mechanism underlying crystal plasticity, is critical for the hardening, processing, and application of a wide range of structural and functional materials[1,7,8]. For decades, the movement of dislocations has been widely observed in crystalline solids under mechanical loading[9-11]. However, the goal of manipulating dislocation motion via a non-mechanical field alone remains elusive. Here, we present real-time observations of dislocation motion controlled solely by an external electric field in single-crystalline zinc sulfide (ZnS). We find that 30{\deg} partial dislocations can move back and forth depending on the direction of the electric field, while 90{\deg} partial dislocations are motionless. We reveal the nonstoichiometric nature of dislocation cores using atomistic imaging and determine their charge characteristics by density functional theory calculations. The glide barriers of charged 30{\deg} partial dislocations, which are lower than those of 90{\deg} partial dislocations, further decrease under an electric field, explaining the experimental observations. This study provides direct evidence of dislocation dynamics under a non-mechanical stimulus and opens up the possibility of modulating dislocation-related properties., Comment: https://www.nature.com/articles/s41563-023-01572-7

We present an effective and efficient method that explores the properties of Transformers in the frequency domain for high-quality image deblurring. Our method is motivated by the convolution theorem that the correlation or convolution of two signals in the spatial domain is equivalent to an element-wise product of them in the frequency domain. This inspires us to develop an efficient frequency domain-based self-attention solver (FSAS) to estimate the scaled dot-product attention by an element-wise product operation instead of the matrix multiplication in the spatial domain. In addition, we note that simply using the naive feed-forward network (FFN) in Transformers does not generate good deblurred results. To overcome this problem, we propose a simple yet effective discriminative frequency domain-based FFN (DFFN), where we introduce a gated mechanism in the FFN based on the Joint Photographic Experts Group (JPEG) compression algorithm to discriminatively determine which low- and high-frequency information of the features should be preserved for latent clear image restoration. We formulate the proposed FSAS and DFFN into an asymmetrical network based on an encoder and decoder architecture, where the FSAS is only used in the decoder module for better image deblurring. Experimental results show that the proposed method performs favorably against the state-of-the-art approaches. Code will be available at \url{https://github.com/kkkls/FFTformer}., Comment: Code will be available at \url{https://github.com/kkkls/FFTformer}

Recently various topological polar structures have been discovered in oxide thin films. Despite the increasing evidence of their switchability under electrical and/or mechanical fields, the dynamic property of isolated ones, which is usually required for applications such as data storage, is still absent. Here, we show the controlled nucleation and motion of isolated three-fold vertices under an applied electric field. At the PbTiO3/SrRuO3 interface, a two-unit-cell thick SrTiO3 layer provides electrical boundary conditions for the formation of three-fold vertices. Utilizing the SrTiO3 layer and in situ electrical testing system, we find that isolated three-fold vertices can move in a controllable and reversible manner with a velocity up to ~629 nm s-1. Microstructural evolution of the nucleation and propagation of isolated three-fold vertices is further revealed by phase-field simulations. This work demonstrates the ability to electrically manipulate isolated three-fold vertices, shedding light on the dynamic property of isolated topological polar structures.

Room-temperature polar skyrmion bubbles that are recently found in oxide superlattice, have received enormous interests for their potential applications in nanoelectronics due to the nanometer size, emergent chirality, and negative capacitance. For practical applications, the ability to controllably manipulate them by using external stimuli is prerequisite. Here, we study the dynamics of individual polar skyrmion bubbles at the nanoscale by using in situ biasing in a scanning transmission electron microscope. The reversible electric field-driven phase transition between topological and trivial polar states are demonstrated. We create, erase and monitor the shrinkage and expansion of individual polar skyrmions. We find that their transition behaviors are substantially different from that of magnetic analogue. The underlying mechanism is discussed by combing with the phase-field simulations. The controllable manipulation of nanoscale polar skyrmions allows us to tune the dielectric permittivity at atomic scale and detailed knowledge of their phase transition behaviors provides fundamentals for their applications in nanoelectronics.

We propose a novel and theoretical model, blocked and hierarchical variational autoencoder (BHiVAE), to get better-disentangled representation. It is well known that information theory has an excellent explanatory meaning for the network, so we start to solve the disentanglement problem from the perspective of information theory. BHiVAE mainly comes from the information bottleneck theory and information maximization principle. Our main idea is that (1) Neurons block not only one neuron node is used to represent attribute, which can contain enough information; (2) Create a hierarchical structure with different attributes on different layers, so that we can segment the information within each layer to ensure that the final representation is disentangled. Furthermore, we present supervised and unsupervised BHiVAE, respectively, where the difference is mainly reflected in the separation of information between different blocks. In supervised BHiVAE, we utilize the label information as the standard to separate blocks. In unsupervised BHiVAE, without extra information, we use the Total Correlation (TC) measure to achieve independence, and we design a new prior distribution of the latent space to guide the representation learning. It also exhibits excellent disentanglement results in experiments and superior classification accuracy in representation learning.

The ability to controllably manipulate the complex topological polar configurations, such as polar flux-closure via external stimuli, enables many applications in electromechanical devices and nanoelectronics including high-density information storage. Here, by using the atomically resolved in situ scanning transmission electron microscopy, we find that a polar flux-closure structure in PbTiO3/SrTiO3 superlattices films can be reversibly switched to ordinary mono ferroelectric c domain or a domain under electric field or stress. Specifically, the electric field initially drives the flux-closure move and breaks them to form intermediate a/c striped domains, while the mechanical stress firstly starts to squeeze the flux-closures to convert into small vortices at the interface and form a continues dipole wave. After the removal of the external stimuli, the flux-closure structure spontaneously returns. Our atomic study provides valuable insights into understanding the lattice-charge interactions and the competing interactions balance in these complex topological structures. Such reversible switching between the flux-closure and ordinary ferroelectric domains also provides the foundation for applications such as memories and sensors., Comment: 22 pages, 5figures

Van der Waals (vdW) layered materials have rather weaker interlayer bonding than the intra-layer bonding, therefore the exfoliation along the stacking direction enables the achievement of monolayer or few layers vdW materials with emerging novel physical properties and functionalities. The ferroelectricity in vdW materials recently attracts renewed interest for the potential use in high-density storage devices. As the thickness going thinner, the competition between the surface energy, depolarization field and interfacial chemical bonds may give rise to the modification of ferroelectricity and crystalline structure, which has limited investigations. In this work, combining the piezoresponse force microscope scanning, contact resonance imaging, we report the existence of the intrinsic in-plane polarization in vdW ferroelectrics CuInP2S6 (CIPS) single crystals, whereas below a critical thickness between 90-100 nm, the in-plane polarization disappears. The Young's modulus also shows an abrupt stiffness at the critical thickness. Based on the density functional theory calculations, we ascribe these behaviors to a structural phase transition from monoclinic to trigonal structure, which is further verified by transmission electron microscope technique. Taken together, these findings demonstrate the foundational importance of structural phase transition for enhancing the rich functionality and broad utility of vdW ferroelectrics.

Domain walls are of increasing interest in ferroelectrics because of their unique properties and potential applications in future nanoelectronics. However, the thickness of ferroelastic domain walls remains elusive due to the challenges in experimental characterization. Here, we determine the atomic structure of ferroelastic domain walls and precisely measure the polarization and domain wall thickness at picometer scale using annular bright field imaging in an aberration-corrected scanning transmission electron microscope. We find that the domain wall thickness in PbZr0.2Ti0.8O3 and PbTiO3 thin films is typically about one-unit cell, across which the oxygen octahedron distortion behavior is in excellent agreement with first principles calculations. Remarkably, wider domain walls about two-unit cells in thickness are also observed for those domains walls are coupled with dislocations and underwent a compressive strain. These results suggest that the thickness of ferroelastic domain walls highly depends on their atomic environments. This study can help us to understand the past debatable experimental results and provide further insights into control of domain walls via strain engineering for their possible applications in nanoelectronics.

Ferroelectric vortices formed through complex lattice-charge interactions have great potential in applications for future nanoelectronics such as memories. For practical applications, it is crucial to manipulate these topological states under external stimuli. Here, we apply mechanical loads to locally manipulate the vortices in a PbTiO3-SrTiO3 superlattice via atomically resolved in situ scanning transmission electron microscopy. The vortices undergo a transition to the a-domain with in-plane polarization under external compressive stress and spontaneously recover after removal of the stress. We reveal the detailed transition process at the atomic scale and reproduce this numerically using phase-field simulations. These findings provide new pathways to control the exotic topological ferroelectric structures for future nanoelectronics and also valuable insights into understanding of lattice-charge interactions at nanoscale.

Dislocations in perovskite oxides have important impacts on their physical and chemical properties, which are determined by their unique atomic environments. In the present study, the structure of dislocations in a 10{\deg} low-angle grain boundary of SrTiO3 (STO) is characterized by spherical aberration corrected scanning transmission electron microscopy (Cs-STEM) and spectroscopy. In contrast to previous studies, the deficiency instead of enrichment of titanium (Ti) is observed at the dislocation cores mainly due to the Sr substitution and under occupancy of Ti. The presence of oxygen vacancies and partially reduced Ti are also detected at the Ti deficient dislocations cores. These findings indicate the atomic structure of dislocations can be very different even they have the same Burges vectors. Controllable elemental segregation in the dislocations and grain boundaries via bicrystal engineering should be very useful for design of devices with novel functions., Comment: 14 pages, 4 figures