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We used an effective Lagrangian approach to investigate the production of the anticharmed pentaquark state $P_{\bar{c}s}^{(*)-}$ with the minimal quark content $\bar{c}sudd$ in the $\Lambda_b^0$ decay and its decay into the $\Lambda D^{(\ast)-}$ and $\Sigma D^{(\ast)-}$. In our calculation, the $P_{\bar{c}s}^{(*)-}$ is considered as the molecule state of the $nD_{s0(s1)}^{-}$ in an $S$ wave, and its production and decay occur via triangle loops at the hadron level. The predicted branching fractions for the processes $\Lambda_b^0\to P_{\bar{c}s}^{(*)-} D^+ $ are around $10^{-4}\sim 10^{-3}$. The partial decay widths of the $ P_{\bar{c}s}^{(*)-} \to \Lambda D^{(\ast)-}$ are between $0.1$ and $\sim 10~\mathrm{MeV}$, whereas the decay widths of the $ P_{\bar{c}s}^{(*)-} \to \Sigma D^{(\ast)-}$ are about one order of magnitude smaller. It is found that the width ratios for the $P_{\bar{c}s}^{(*)-}$ decays are nearly independent of the model parameter $\alpha$. It is hoped that these predictions could be helpful in searching for the anticharmed-strange pentaquark candidates in future LHCb experiments., Comment: 10 pages, 6 figures, update the Pcs* mass to be about 3.44 GeV

Iodinated contrast agents are widely utilized in numerous interventional procedures, yet posing substantial health risks to patients. This paper presents CAS-GAN, a novel GAN framework that serves as a ``virtual contrast agent" to synthesize X-ray angiographies via disentanglement representation learning and vessel semantic guidance, thereby reducing the reliance on iodinated agents during interventional procedures. Specifically, our approach disentangles X-ray angiographies into background and vessel components, leveraging medical prior knowledge. A specialized predictor then learns to map the interrelationships between these components. Additionally, a vessel semantic-guided generator and a corresponding loss function are introduced to enhance the visual fidelity of generated images. Experimental results on the XCAD dataset demonstrate the state-of-the-art performance of our CAS-GAN, achieving a FID of 5.94 and a MMD of 0.017. These promising results highlight CAS-GAN's potential for clinical applications., Comment: 8 pages, 4 figures

Many-body interactions in metal-organic frameworks are fundamental for emergent quantum physics. Unlike their solution counterpart, magnetization at surfaces in low-dimensional analogues is strongly influenced by magnetic anisotropy induced by the substrate and still not well understood. Here, we use on-surface coordination chemistry to synthesize on Ag(111) and superconducting Pb(111) an iron-based spin chain by using pyrene-4,5,9,10-tetraone precursors as ligands. Using low-temperature scanning probe microscopy, we compare their structures and low-energy spin excitations of coordinated Fe atoms with high S = 2 spin-state. Although the chain and coordination centers are identical on both substrates, the long-range spin-spin coupling due to a superexchange through the ligand observed on Ag is absent on Pb(111). We ascribe this reduction of spin-spin interactions on Pb to the depletion of electronic states around the Fermi level in the Pb(111) superconductor as compared to silver.

HfO$_2$-based ferroelectrics have emerged as promising materials for advanced nanoelectronics, with their robust polarization and silicon compatibility making them ideal for high-density, non-volatile memory applications. Oxygen vacancies, particularly in positively charged states, are suggested to profoundly impact the polymorphism kinetics and phase stability of hafnia, thereby affecting its ferroelectric behavior. The electronic structures of pristine and oxygen-deficient hafnia polymorph have been extensively studied using density functional theory, primarily employing (semi-)local exchange-correlation functionals. However, these methods often underestimate band gaps and may not accurately capture the localized nature of $d$-electrons. In this work, we investigate hafnia in various phases using DFT + $U$ + $V$, with onsite $U$ and intersite $V$ Hubbard parameters computed self-consistently via the pseudohybrid Hubbard density functional, ACBN0, and its extended version eACBN0. We find that the self-consistent DFT + $U$ method provides comparable accuracy to the computationally more expensive Heyd-Scuseria-Ernzerhof (HSE) hybrid density functional in predicting relative thermodynamic stability, band gaps, and density of states. Furthermore, it is a cost-effective approach for estimating the formation energies of oxygen vacancies. Additionally, we demonstrate that environmentally dependent Hubbard parameters serve as useful indicators for analyzing bond strengths and electronic structures in real space.

Many dipolar topological structures have been experimentally demonstrated in (PbTiO$_3$)$_n$/(SrTiO$_3$)$_n$ superlattices, such as flux-closure, vortice, and skyrmion. In this work, we employ deep potential molecular dynamics (MD) to investigate the dynamical response of the (PbTiO$_3$)$_{10}$/(SrTiO$_3$)$_{10}$ superlattice, supporting polar vortex arrays, to temperature and electric field at the atomic level. Our simulations reveal a unique phase transition sequence from ferroelectric-like to antiferroelectric-like to paraelectric in the in-plane direction as temperature increases. In the ferroelectric-like state, we observe field-driven reversible switching of in-plane polarization coupled with out-of-plane movements of vortex cores during MD simulations. In the antiferroelectric-like region, the polarization-electric field hysteresis loop exhibits a superparaelectric feature, showing nearly no loss. This behavior is attributed to a strong recovering force to form polar vortex arrays, dictated by the electrical and mechanical boundary conditions within the superlattice. The (PbTiO$_3$)$_{10}$/(SrTiO$_3$)$_{10}$ superlattice in the antiferroelectric-like state also demonstrates large in-plane susceptibility and tunability. The effect of Pb doping in the SrTiO$_3$ layer on the topological structural transition in the superlattice is investigated. The weakened depolarization field in the PbTiO$_3$ layers leads to new dipolar configurations, such as enlarged skyrmion bubble within $c$ domains in (PbTiO$_3$)$_{10}$/(Pb$_{0.4}$Sr$_{0.6}$TiO$_3$)$_{10}$, and we quantify their thermal and electrical responses through MD simulations. These quantitative atomistic insights will be useful for the controlled optimization of perovskite superlattices for various device applications., Comment: 8 figures

Recent advancements in pre-trained Vision-Language Models (VLMs) have highlighted the significant potential of prompt tuning for adapting these models to a wide range of downstream tasks. However, existing prompt tuning methods typically map an image to a single representation, limiting the model's ability to capture the diverse ways an image can be described. To address this limitation, we investigate the impact of visual prompts on the model's generalization capability and introduce a novel method termed Multi-Representation Guided Prompt Tuning (MePT). Specifically, MePT employs a three-branch framework that focuses on diverse salient regions, uncovering the inherent knowledge within images which is crucial for robust generalization. Further, we employ efficient self-ensemble techniques to integrate these versatile image representations, allowing MePT to learn all conditional, marginal, and fine-grained distributions effectively. We validate the effectiveness of MePT through extensive experiments, demonstrating significant improvements on both base-to-novel class prediction and domain generalization tasks.

Magnetic impurities on superconductors present a viable platform for building advanced applications in quantum technologies. However, a controlled manipulation of their quantum states continues to pose a significant challenge, hindering the progress in the field. Here we show the manipulation of magnetic states in the radical molecule 4,5,9,10-tetrabromo-1,3,6,8-tetraazapyrene (TBTAP) on a Pb(111) superconducting surface using low-temperature scanning tunneling microscopy. Tunneling spectra reveal Yu-Shiba-Rusinov (YSR) states near the Fermi energy in isolated molecules. A quantum phase transition from singlet to doublet ground state is induced by changing the tip-molecule distance. Additionally, the presence of a second TBTAP molecule allows tuning of the YSR state position by altering the relative distance and can induce splitting of the YSR states for certain orientations. The construction of molecular chains up to pentamers shows periodic arrangements of charged and neutral molecules, with even-numbered chains forming a charged dimer structure at one end. Information can be encoded in these chains by switching the dimer position. These findings elucidate interactions between molecular assemblies and superconducting substrates, paving the way for advanced quantum-state engineering.

Existing Large Vision-Language Models (LVLMs) primarily align image features of vision encoder with Large Language Models (LLMs) to leverage their superior text generation capabilities. However, the scale disparity between vision encoder and language model may led to LLMs assuming a predominant role in multi-modal comprehension. This imbalance in LVLMs may result in the instances of hallucinatory. Concretely, LVLMs may generate consistent descriptions with or without visual input, indicating that certain outputs are influenced solely by context text. We refer to this phenomenon as "text inertia." To counteract this issue, we introduce a training-free algorithm to find an equilibrium point between image comprehension and language inference. Specifically, we adaptively involve adjusting and amplifying the attention weights assigned to image tokens, thereby granting greater prominence to visual elements. Meanwhile, we subtract the logits of multi-modal inputs from ones of pure text input, which can help LVLMs be not biased towards LLMs. By enhancing images tokens and reducing the stubborn output of LLM, we can let LVLM pay more attention to images, towards alleviating text inertia and reducing the hallucination in LVLMs. Our extensive experiments shows that this method substantially reduces the frequency of hallucinatory outputs in various LVLMs in terms of different metrics. Project page is available at https://lalbj.github.io/projects/PAI/.

The electronic bandgap of a material is often fixed after fabrication. The capability to realize on-demand and non-volatile control over the bandgap will unlock exciting opportunities for adaptive devices with enhanced functionalities and efficiency. We introduce a general design principle for on-demand and non-volatile control of bandgap values, which utilizes reversible sliding-induced polarization driven by an external electric field to modulate the irreversible background polarization in asymmetric two-dimensional (2D) multilayers. The structural asymmetry can be conveniently achieved in homobilayers of Janus monolayers and heterobilayers of nonpolar monolayers, making the design principle applicable to a broad range of 2D materials. We demonstrate the versatility of this design principle using experimentally synthesized Janus metal dichalcogenide (TMD) multilayers as examples. Our first-principles calculations show that the bandgap modulation can reach up to 0.3 eV and even support a semimetal-to-semiconductor transition. By integrating a ferroelectric monolayer represented by 1T$'''$-MoS$_2$ into a bilayer, we show that the combination of intrinsic ferroelectricity and sliding ferroelectricity leads to multi-bandgap systems coupled to multi-step polarization switching. The sliding-reversible bandgap modulation offers an avenue to dynamically adjust the optical, thermal, and electronic properties of 2D materials through mechanical and electrical stimuli., Comment: 21 pages, 5 figures

Hafnia-based ferroelectrics hold promise for nonvolatile ferroelectric memory devices. However, the high coercive field required for polarization switching remains a prime obstacle to their practical applications. A notable reduction in coercive field has been achieved in ferroelectric Hf(Zr)$_{1+x}$O$_2$ films with interstitial Hf(Zr) dopants [Science 381, 558 (2023)], suggesting a less-explored strategy for coercive field optimization. Supported by density functional theory calculations, we demonstrate the $Pca2_1$ phase, with a moderate concentration of interstitial Hf dopants, serves as a minimal model to explain the experimental observations, rather than the originally assumed rhombohedral phase. Large-scale deep potential molecular dynamics simulations suggest that interstitial defects promote the polarization reversal by facilitating $Pbcn$-like mobile 180$^\circ$ domain walls. A simple pre-poling treatment could reduce the switching field to less than 1 MV/cm and enable switching on a subnanosecond timescale. High-throughput calculations reveal a negative correlation between the switching barrier and dopant size and identify a few promising interstitial dopants for coercive field reduction.

The Belle collaboration recently reported a promising candidate for the spin-2 $D^*\bar{D}^*$ partner of the $X(3872)$, called the $X_2$ for short, having a mass of $(4014.3 \pm 4.0 \pm 1.5)~\mathrm{MeV}$ and a width of $(4 \pm 11 \pm 6)~\mathrm{MeV} $. In present work, we assume the $X_2$ as a pure molecule of the $D^*\bar{D}^*$ under three cases, i.e., pure neutral components ($\theta = 0$), isospin singlet ($\theta = \pi/4$) and neutral components dominant ($\theta = \pi/6$), where $\theta$ is a phase angle describing the proportion of neutral and charged constituents. Using an effective Lagrangian approach, we calculated the partial widths of $X_2\to VV$ and $X_2 \to PP$ ($V$ and $P$ stand for light vector and pseudoscalar mesons, respectively). The predicted decay widths of $X_2 \to VV$ can reach a few hundreds of $\mathrm{keV}$, while the decay widths of $X_2 \to PP$ are about several tens of $\mathrm{keV}$. In addition, the effects from the proportion of neutral and charged constituent on the decay widths of $X_2\to VV$ and $PP$ are also investigated. We hope that the present calculations will be checked experimentally in the future., Comment: 8 pages, 7figure, comments welcome

Automatic vessel segmentation is paramount for developing next-generation interventional navigation systems. However, current approaches suffer from suboptimal segmentation performances due to significant challenges in intraoperative images (i.e., low signal-to-noise ratio, small or slender vessels, and strong interference). In this paper, a novel spatial-frequency learning and topological channel interaction network (SPIRONet) is proposed to address the above issues. Specifically, dual encoders are utilized to comprehensively capture local spatial and global frequency vessel features. Then, a cross-attention fusion module is introduced to effectively fuse spatial and frequency features, thereby enhancing feature discriminability. Furthermore, a topological channel interaction module is designed to filter out task-irrelevant responses based on graph neural networks. Extensive experimental results on several challenging datasets (CADSA, CAXF, DCA1, and XCAD) demonstrate state-of-the-art performances of our method. Moreover, the inference speed of SPIRONet is 21 FPS with a 512x512 input size, surpassing clinical real-time requirements (6~12FPS). These promising outcomes indicate SPIRONet's potential for integration into vascular interventional navigation systems. Code is available at https://github.com/Dxhuang-CASIA/SPIRONet.

With rapid advances in network hardware, far memory has gained a great deal of traction due to its ability to break the memory capacity wall. Existing far memory systems fall into one of two data paths: one that uses the kernel's paging system to transparently access far memory at the page granularity, and a second that bypasses the kernel, fetching data at the object granularity. While it is generally believed that object fetching outperforms paging due to its fine-grained access, it requires significantly more compute resources to run object-level LRU and eviction. We built Atlas, a hybrid data plane enabled by a runtime-kernel co-design that simultaneously enables accesses via these two data paths to provide high efficiency for real-world applications. Atlas uses always-on profiling to continuously measure page locality. For workloads already with good locality, paging is used to fetch data, whereas for those without, object fetching is employed. Object fetching moves objects that are accessed close in time to contiguous local space, dynamically improving locality and making the execution increasingly amenable to paging, which is much more resource-efficient. Our evaluation shows that Atlas improves the throughput (e.g., by 1.5x and 3.2x) and reduces the tail latency (e.g., by one and two orders of magnitude) when using remote memory, compared with AIFM and Fastswap, the state-of-the-art techniques respectively in the two categories.

Despite being a powerful concept, distributed shared memory (DSM) has not been made practical due to the extensive synchronization needed between servers to implement memory coherence. This paper shows a practical DSM implementation based on the insight that the ownership model embedded in programming languages such as Rust automatically constrains the order of read and write, providing opportunities for significantly simplifying the coherence implementation if the ownership semantics can be exposed to and leveraged by the runtime. This paper discusses the design and implementation of DistR, a Rust-based DSM system that outperforms the two state-of-the-art DSM systems GAM and Grappa by up to 2.64x and 29.16x in throughput, and scales much better with the number of servers.

We investigated the pionic transitions between the $X_2$ [spin-2 partner of the $X(3872)$] and $\chi_{c1,2}$ using a nonrelativistic effective field theory. The $X_2$ is assumed to be a bound state of the $D^{*}$ and $\bar{D}^*$ mesons and to decay through several kinds of loops, including the bubble, triangle and box loops. Within the present model, the widths for the single-pion decays $X_2\to\pi^0\chi_{cJ}$ are predicted to be about $3$--$30$ keV. For the dipion decays, the widths are a few keVs. These widths yield a branching fraction of $10^{-3}$--$10^{-2}$. The ratio $R_{\mathrm{c}0}=\Gamma (X_2\to\pi^+\pi^-\chi_{cJ})/\Gamma (X_2\to\pi^0\pi^0\chi_{cJ}) \simeq 1.6$, which is a bit smaller than the expected value of $2$, and $R_{21}=\Gamma (X_2\to\pi\pi\chi_{c2})/\Gamma (X_2\to\pi\pi\chi_{c1}) \simeq 0.85$. These ratios are nearly independent of the $X_2$ mass and the coupling constants, which might be a good quantity for the experiments. Moreover, the invariant mass spectra of the $\pi^0\chi_{cJ}$ final state for the dipion processes are presented, showing a cusp structure at the $D {\bar D}^*$ threshold enhanced and narrowed by the nearby triangle singularity., Comment: 12 pages, 9 figure, Comments welcome; Correct the last Paragraph of Introduction, accepted by PRD

Since the first report of ferroelectricity in nanoscale HfO$_2$-based thin films in 2011, this silicon-compatible binary oxide has quickly garnered intense interest in academia and industry, and continues to do so. Despite its deceivingly simple chemical composition, the ferroelectric physics supported by HfO$_2$ is remarkably complex, arguably rivaling that of perovskite ferroelectrics. Computational investigations, especially those utilizing first-principles density functional theory (DFT), have significantly advanced our understanding of the nature of ferroelectricity in these thin films. In this review, we provide an in-depth discussion of the computational efforts to understand ferroelectric hafnia, comparing various metastable polar phases and examining the critical factors necessary for their stabilization. The intricate nature of HfO$_2$ is intimately related to the complex interplay among diverse structural polymorphs, dopants and their charge-compensating oxygen vacancies, and unconventional switching mechanisms of domains and domain walls, which can sometimes yield conflicting theoretical predictions and theoretical-experimental discrepancies. We also discuss opportunities enabled by machine-learning-assisted molecular dynamics and phase-field simulations to go beyond DFT modeling, probing the dynamical properties of ferroelectric HfO$_2$ and tackling pressing issues such as high coercive fields.

Possible light bosonic dark matter interactions with the Standard Model photon have been searched by microwave resonant cavities. In this paper, we demonstrate the cryogenic readout system calibration of a 7.138 GHz copper cavity with a loaded quality factor $Q_l=10^4$, operated at 22 mK temperature based on a dilution refrigerator. Our readout system consists of High Electron Mobility Transistors as cryogenic amplifiers at 4 K, plus room-temperature amplifiers and a spectrum analyzer for signal power detection. We test the system with a superconducting two-level system as a single-photon source in the microwave frequency regime and report an overall 95.6 dB system gain and -71.4 dB attenuation in the cavity's input channel. The effective noise temperature of the measurement system is 7.5 K., Comment: 7 pages, 5 figures, version to appear in CPC

This study adopts the fixed effects panel model and provincial panel data on anticorruption and the innovation efficiency of high-level technology and new technology enterprises in China from 2005 to 2014, to estimate the effects of the anticorruption movement on the innovation efficiency of enterprises at different corruption levels. The empirical results show that anticorruption is positively correlated with the innovation efficiency of enterprises; however, the correlation is differentiated according to different corruption levels and business natures. At a high level of corruption, anticorruption has positive impacts on enterprises' innovation; at a low level of corruption, it negatively affects innovation efficiency. However, anticorruption has negative effects on the innovation efficiency of state-owned enterprises at both high and low corruption levels; for nonstate-owned enterprises, its effects are positive at a high corruption level and negative at a low corruption level. The effects remain the same across different regions.

The dark photon is a promising candidate for the dark matter which comprises most of the matter in our visible Universe. Via kinetic mixing with the Standard Model it can also be resonantly converted to photons in an electromagnetic cavity, offering novel experimental possibilities for the discovery and study of dark matter. We report the results of a pathfinder dark photon dark matter cavity search experiment performed at Hunan Normal University and the Institute of Physics, Chinese Academy of Sciences, representing the first stage of the APEX (Axion and dark Photon EXperiment) program. Finding no statistically significant excess, we place an upper limit on the kinetic mixing parameter $|\chi|<3.7\times 10^{-13}$ around $m_A\simeq 29.5$ $\mu$eV at 90% confidence level. This result exceeds other constraints on dark photon dark matter in this frequency range by roughly an order of magnitude., Comment: 5 pages, 4 figures. Updated to match journal version

The high coercive field ($\mathcal{E}_c$) of hafnia-based ferroelectrics presents a major obstacle to their applications. The ferroelectric switching mechanisms in hafnia that dictate $\mathcal{E}_c$, especially those related to nucleation-and-growth at the domain wall (DW), have remained elusive. Through deep-learning-assisted multiscale simulations, we determine the finite-temperature thermodynamics and switching mechanisms for diverse types of 180$^\circ$ DWs, revealing a complex, stress-sensitive mobility landscape. The propagation velocities for mobile DW types under various thermal conditions can be characterized with a single creep equation, featuring a creep exponent of 2. This unconventional critical exponent results from the nucleation of a half-unit-cell-thin, elliptically-shaped critical nucleus. Our multiscale approach not only reproduces the experimental thickness ($d$) scaling, $\mathcal{E}_c\propto d^{-\frac{2}{3}}$, but also predicts that $\mathcal{E}_c$ of HfO$_2$ can be engineered to $\approx$0.1 MV/cm, even lower than perovskite ferroelectrics. The theoretical lower bound of $\mathcal{E}_c$ afforded by ferroelectric hafnia offers opportunities to realize power-efficient, high-fidelity ferroelectric nanoelectronics., Comment: 19 pages, 4 figures

In the realm of ferroelectric memories, HfO2-based ferroelectrics stand out because of their exceptional CMOS compatibility and scalability. Nevertheless, their switchable polarization and switching speed are not on par with those of perovskite ferroelectrics. It is widely acknowledged that defects play a crucial role in stabilizing the metastable polar phase of HfO2. Simultaneously, defects also pin the domain walls and impede the switching process, ultimately rendering the sluggish switching of HfO2. Herein, we present an effective strategy involving acceptor-donor co-doping to effectively tackle this dilemma. Remarkably enhanced ferroelectricity and the fastest switching process ever reported among HfO2 polar devices are observed in La3+-Ta5+ co-doped HfO2 ultrathin films. Moreover, robust macro-electrical characteristics of co-doped films persist even at a thickness as low as 3 nm, expanding potential applications of HfO2 in ultrathin devices. Our systematic investigations further demonstrate that synergistic effects of uniform microstructure and smaller switching barrier introduced by co-doping ensure the enhanced ferroelectricity and shortened switching time. The co-doping strategy offers an effective avenue to control the defect state and improve the ferroelectric properties of HfO2 films.

We studied the radiative transitions between the $\Upsilon(10753)$, the $S$-$D$ mixed state of the $\Upsilon(4S)$ and $\Upsilon_1(3\,{}^3D_1)$, and the $X_b$, the heavy quark flavor symmetry counterpart of the $X(3782)$ in the bottomonium sector. The radiative transition was assumed to occur through the intermediate bottom mesons, including $P$-wave $B_1^{(\prime)}$ mesons as well as the $S$-wave $B^{(*)}$ ones. The consideration of the $B_1^{(\prime)}$ mesons leads to the couplings to be in $S$-wave, and hence enhances the contributions of the intermediate meson loops. The radiative decay width for the $\Upsilon(10753)\to\gamma X_b$ is predicted to be order of $10~\mathrm{keV}$, corresponding to a branching fraction of $10^{-4}$. Based on the theoretical results, we strongly suggest to search for the $X_b$ in the $e^+e^-\to\gamma X_b$ with $X_b\to\pi\pi\chi_{b1}$ near $\sqrt{s}=10.754~\mathrm{GeV}$, and it is hoped that the calculations here could be tested by the future Belle II experiments., Comment: 7 pages, 4 figures, accepted by PRD(20240510)

Medical image segmentation takes an important position in various clinical applications. Deep learning has emerged as the predominant solution for automated segmentation of volumetric medical images. 2.5D-based segmentation models bridge computational efficiency of 2D-based models and spatial perception capabilities of 3D-based models. However, prevailing 2.5D-based models often treat each slice equally, failing to effectively learn and exploit inter-slice information, resulting in suboptimal segmentation performances. In this paper, a novel Momentum encoder-based inter-slice fusion transformer (MOSformer) is proposed to overcome this issue by leveraging inter-slice information at multi-scale feature maps extracted by different encoders. Specifically, dual encoders are employed to enhance feature distinguishability among different slices. One of the encoders is moving-averaged to maintain the consistency of slice representations. Moreover, an IF-Swin transformer module is developed to fuse inter-slice multi-scale features. The MOSformer is evaluated on three benchmark datasets (Synapse, ACDC, and AMOS), establishing a new state-of-the-art with 85.63%, 92.19%, and 85.43% of DSC, respectively. These promising results indicate its competitiveness in medical image segmentation. Codes and models of MOSformer will be made publicly available upon acceptance., Comment: Under Review

Freestanding ferroelectric oxide membranes emerge as a promising platform for exploring the interplay between topological polar ordering and dipolar interactions that are continuously tunable by strain. Our investigations combining density functional theory (DFT) and deep-learning-assisted molecular dynamics simulations demonstrate that DFT-predicted strain-driven morphotropic phase boundary involving monoclinic phases manifest as diverse domain structures at room temperatures, featuring continuous distributions of dipole orientations and mobile domain walls. Detailed analysis of dynamic structures reveals that the enhanced piezoelectric response observed in stretched PbTiO$_3$ membranes results from small-angle rotations of dipoles at domain walls, distinct from conventional polarization rotation mechanism and adaptive phase theory inferred from static structures. We identify a ferroelectric topological structure, termed "dipole spiral," which exhibits a giant intrinsic piezoelectric response ($>$320 pC/N). This helical structure, possessing a rotational zero-energy mode, unlocks new possibilities for exploring chiral phonon dynamics and dipolar Dzyaloshinskii-Moriya-like interactions.

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

We demonstrate that sliding motion between two layers of the newly discovered ferroelectric and topologically trivial bismuth (Bi) monolayer [Nature 617, 67 (2023)] can induce a sequence of topological phase transitions, alternating between trivial and nontrivial states. Interestingly, a lateral shift, even when preserving spatial symmetry, can still switch the quantum spin Hall state on and off. The substantial band-gap modulation and band inversion due to interlayer sliding arise primarily from the intralayer in-plane charge transfer processes involving Bi atoms at the outermost atomic layers, rather than the interlayer charge redistribution. We map out the topological phase diagram and the geometric Berry curvature-dipole induced nonlinear anomalous Hall response resulting from sliding, highlighting the potential for robust mechanical control over the edge current and the Hall current. Bilayer configurations that are $\mathbb{Z}_2$ nontrivial can produce drastically different transverse currents orthogonal to the external electric field. This occurs because both the direction and magnitude of the Berry curvature dipole at the Fermi level depend sensitively on the sliding displacement. Our results suggest that bilayer bismuth could serve as a platform to realize power-efficient ``Berry slidetronics" for topology memory applications., Comment: 8 pages, 4 figures

Two-dimensional (2D) ferroelectrics offer the potential for ultrathin flexible nanoelectronics, typically utilizing a metal-ferroelectric-metal sandwich structure as the functional unit. Electrodes can either contribute free carriers to screen the depolarization field, enhancing nanoscale ferroelectricity, or they can induce charge doping, disrupting the long-range crystalline order. Here, we explore the dual roles of electrodes in 2D ferroelectric capacitors, supported by extensive first-principles calculations covering a range of electrode work functions. Our results reveal volcano-type relationships between ferroelectric-electrode binding affinity and work function, which are further unified by a quadratic scaling between the binding energy and the transferred interfacial charge. At the monolayer limit, the charge transfer dictates the ferroelectric stability and switching properties. This is a general characteristic confirmed in various 2D ferroelectrics including $\alpha$-In$_2$Se$_3$, CuInP$_2$S$_6$, and SnTe. As the ferroelectric layer's thickness increases, the stability of the capacitor evolves from a charge transfer-dominated to a screening-dominated state. The delicate interplay between these two effects will have important implications for the applications of 2D ferroelectric capacitors.

Topological superconductivity emerges in chains or arrays of magnetic atoms coupled to a superconductor. However, the external controllability of such systems with gate voltages is detrimental for their future implementation in a topological quantum computer. Here we showcase the supramolecular assembly of radical molecules on Pb(111), whose discharge is controlled by the tip of a scanning tunneling microscope. Charged molecules carry a spin-1/2 state, as confirmed by observing Yu-Shiba-Rusinov in-gap states by tunneling spectroscopy at millikelvin temperature. Low energy modes are localized at island boundaries with a long decay towards the interior, whose spectral signature is consistent with Majorana zero modes protected by mirror symmetry. Our results open up a vast playground for the synthesis of gate-tunable organic topological superconductors., Comment: 21 pages 5 figures

Offline reinforcement learning (RL) aims to optimize policy using collected data without online interactions. Model-based approaches are particularly appealing for addressing offline RL challenges due to their capability to mitigate the limitations of offline data through data generation using models. Prior research has demonstrated that introducing conservatism into the model or Q-function during policy optimization can effectively alleviate the prevalent distribution drift problem in offline RL. However, the investigation into the impacts of conservatism in reward estimation is still lacking. This paper proposes a novel model-based offline RL algorithm, Conservative Reward for model-based Offline Policy optimization (CROP), which conservatively estimates the reward in model training. To achieve a conservative reward estimation, CROP simultaneously minimizes the estimation error and the reward of random actions. Theoretical analysis shows that this conservative reward mechanism leads to a conservative policy evaluation and helps mitigate distribution drift. Experiments on D4RL benchmarks showcase that the performance of CROP is comparable to the state-of-the-art baselines. Notably, CROP establishes an innovative connection between offline and online RL, highlighting that offline RL problems can be tackled by adopting online RL techniques to the empirical Markov decision process trained with a conservative reward. The source code is available with https://github.com/G0K0URURI/CROP.git.