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Di Zhou

Publications and source records attributed to Di Zhou.

At least 19 recordsLinked to original sources
arXiv2026

Superconducting gap in covalent bismuth dihydride BiH$_2$ under extreme conditions

Hydride superconductors at megabar pressures provide a promising platform for exploring room-temperature superconductivity. However, their superconducting gaps remain largely inaccessible to conventional spectroscopic due to diamond anvil cell constraints and minute sample dimensions. Here we develop a pulsed current method and apply it to covalent BiH$_2$ synthesized at 157--176 GPa. BiH$_2$ exhibits superconductivity at 58--70 K and upper critical fields of 11--17 T, substantially lower than those of many clathrate superhydrides, corresponding to a relatively long coherence length and an experimentally accessible critical current density. Short rectangular pulses minimize sustained Joule heating and enable currents up to 160 mA, allowing $J_c(T)$ to be measured in the low-temperature regime down to 2 K at 176 GPa. The normalized critical-current response remains reproducible between two measurement runs and is better described by a two-scale $s$-wave model than by single-gap $s$- or $d$-wave models, yielding effective energy scales of approximately 6.9 and 1.5 meV. Fully anisotropic Migdal--Eliashberg calculations yield a single highly anisotropic gap, suggesting that the two-gap behavior observed experimentally originates from gap anisotropy rather than two independent gaps. These results establish pulsed critical-current measurements as a practical gap-sensitive transport probe under extreme pressure and, with further increases in peak-current capability, provide a route toward investigating room-temperature hydrides such as La--Sc--H.

cond-mat.supr-con↗
arXiv2026

Stability Analysis of Superconductivity in $\textit{P6/mmm}$-LaSc$_2$H$_{24}$ and its Experimental Reproducibility from La-Sc Alloys

In this work, we analyze the feasibility of room-temperature superconductivity in the lanthanum-scandium hydride $\textit{P6/mmm}$-LaSc$_2$H$_{24}$. We demonstrate that the electron-phonon coupling calculations performed using the $σ$-broadening of the double $δ$-function at the Fermi surface lead to a very strong dependence of $\textit{T$_c$}(σ)$ on the arbitrary $σ$, whereas the tetrahedral method for the electron-phonon interaction is free from this drawback and leads to $\textit{T$_c$}$ > 300 K at 300 GPa in agreement with previous predictions. By analyzing the stability of the metallic state of LaSc$_2$H$_{24}$ at 250-300 GPa, we show that this compound is at the edge of the stability region ($ξ$ = 0.54), similar to $\textit{fcc}$ LaH$_{10}$ at 140-150 GPa. Seven experimental attempts to synthesize LaSc$_2$H$_{24}$ at 250-280 GPa starting from the (La,Sc$_2$) alloy are unsuccessful and indicate the absence of even traces of superconductivity at 245-300 K in all the resulting La-Sc-H hydrides. The method for preparing the precursor by simultaneous deposition of La and Sc metals may be a key factor for the successful synthesis of LaSc$_2$H$_{24}$.

cond-mat.supr-con↗
arXiv2026

Yttrium Superhydrides Revisited: Advanced Experimental and Theoretical Studies of YH$_6$, YH$_9$ and YH$_{10}$

Yttrium polyhydrides are benchmark materials in high-pressure superconductivity, yet several key properties of the Y-H system remain insufficiently characterized. Here we combine contact transport, contactless radio-frequency measurements, pulsed-field experiments, and first-principles calculations to reinvestigate YH$_6$, YH$_9$, and YH$_{10}$ in the pressure range 140-213 GPa. Yttrium hydrides YH$_6$ ($\textit{$T_c$}$ = 218-221 K) and YH$_9$ ($\textit{$T_c$}$ = 235-237 K) demonstrate narrow superconducting transitions ($\textit{$Δ$T$_c$}$ = 2-5 K), approaching the limit imposed by thermal fluctuations. Pulsed-field measurements on YH$_6$ up to 60 T establish an extended superconducting phase diagram with a linear slope $\textit{dB$_{c2}$/dT}$ = -0.52 T/K, pronounced transition broadening above 30 T, and negligible normal-state magnetoresistance. We report the radio-frequency AC susceptibility study of YH$_6$, providing evidence for superconductivity via high-frequency field screening in a contactless geometry. Experiments involving Pd incorporation, Pd thin-film sputtering, and Al alloying show strong suppression of high-temperature superconductivity, with no transitions detected above 78-120 K. Finally, using density-functional theory with the stochastic self-consistent harmonic approximation, superconducting density-functional theory, and full-bandwidth Migdal-Eliashberg calculations, we show that anharmonic effects substantially reduce the predicted $\textit{$T_c$}$ of cubic YH$_{10}$ to approximately 260-270 K. These results strongly disfavor room-temperature superconductivity in binary yttrium superhydrides.

cond-mat.supr-con↗
arXiv2026

Coarse Graining with Neural Operators for Simulating Chaotic Systems

Accurately predicting the long-term behavior of chaotic systems is crucial for various applications such as climate modeling. However, achieving such predictions typically requires iterative computations over a dense spatiotemporal grid to account for the unstable nature of chaotic systems, which is expensive and impractical in many real-world situations. An alternative approach to such a full-resolved simulation is using a coarse grid and then correcting its errors through a \textit{closure model}, which approximates the overall information from fine scales not captured in the coarse-grid simulation. Recently, ML approaches have been used for closure modeling, but they typically require a large number of training samples from expensive fully-resolved simulations (FRS). In this work, we prove an even more fundamental limitation, i.e., the standard approach to learning closure models suffers from a large approximation error for generic problems, no matter how large the model is, and it stems from the non-uniqueness of the mapping. We propose an alternative end-to-end learning approach using a physics-informed neural operator (PINO) that overcomes this limitation by not using a closure model or a coarse-grid solver. We first train the PINO model on data from a coarse-grid solver and then fine-tune it with (a small amount of) FRS and physics-based losses on a fine grid. The discretization-free nature of neural operators means that they do not suffer from the restriction of a coarse grid that closure models face, and they can provably approximate the long-term statistics of chaotic systems. In our experiments, our PINO model achieves a 330x speedup compared to FRS with a relative error $\sim 10\%$. In contrast, the closure model coupled with a coarse-grid solver is $60$x slower than PINO while having a much higher error $\sim186\%$ when the closure model is trained on the same FRS dataset.

cs.LG↗
arXiv2026

3D Topologically Polarized Elastic Metamaterials Enable Asymmetric Energy Isolation at Low Frequencies

Topologically polarized elasticity has been extensively studied in lower-dimensions, yet its three-dimensional (3D) counterpart remains largely unexplored. Here, we demonstrate omnidirectional topological elasticity in 3D structures that incorporate bending stiffness, which elevates zero-frequency topological mechanical states into finite-frequency phononic modes. These modes are localized at a single boundary, creating a pronounced stiffness contrast in both static and finite-frequency dynamic regimes. This three-dimensional structure exhibits highly polarized mechanical behavior across all spatial dimensions, establishing omnidirectional asymmetric topological elasticity. Experimental and numerical results confirm robust, asymmetric energy isolation, arising from the interplay between bulk topological polarization and boundary-localized surface modes. Our findings establish a paradigm for 3D metamaterials, with promising applications in vibration shielding and directional wave manipulation.

cond-mat.soft↗
arXiv2026

Data-driven Sensor Placement for Predictive Applications: A Correlation-Assisted Attribution Framework (CAAF)

Optimal sensor placement (OSP) is critical for efficient, accurate monitoring, control, and inference in complex physical systems. We propose a machine-learning-based feature attribution (FA) framework to identify OSP for target predictions. FA quantifies input contributions to a model output; however, it struggles with highly correlated input data often encountered in practical applications for OSP. To address this, we propose a Correlation-Assisted Attribution Framework (CAAF), which introduces a clustering step on the candidate sensor locations before performing FA to reduce redundancy and enhance generalizability. We first illustrate the core principles of the proposed framework through a series of validation cases, then demonstrate its effectiveness in realistic dynamical systems such as structural health monitoring, airfoil lift prediction, and wall-normal velocity estimation for turbulent channel flow. The results show that the CAAF outperforms alternative approaches that typically struggle due to the presence of nonlinear dynamics, chaotic behavior, and multi-scale interactions, and enables the effective application of FA for identifying OSP in real-world environments.

cs.CE↗
arXiv2026

Radio-Frequency Method for Detecting Superconductivity Under High Pressure

We introduce a contactless technique for probing superconductivity and magnetic ordering transitions in micron-sized samples under extreme pressure. Utilizing a multistage Lenz lens system, directly sputtered onto diamond anvils, we realize a radio-frequency (RF, 50 kHz - 200 MHz) transformer with a sample of 50-100 $μ$m in diameter, as its core. This configuration enables efficient transfer and focusing of an electromagnetic field within the diamond anvil cell's chamber. Consequently, the transmitted RF signal exhibits high sensitivity to variations in the sample's surface conductivity and magnetic permeability. We validate this method by determining the critical temperatures ($T_{\text{c}}$) of known superconductors, including NbTi, MgB$_2$, Hg-1223, Bi-2212, YBCO, and REBCO in various magnetic fields, as well as the magnetic ordering temperatures of Gd and Tb. Notably, we apply this technique to the LaH$_{10-x}$, CeH$_{9-10}$, and (La,Ce)H$_{10-12}$ superhydrides at a pressure of about 1-1.5 Mbar. The observed superconducting transitions in Ce and La superhydrides at 90-110 K and 215-242 K, respectively, correlate with the $T_{\text{c}}$'s determined via traditional electrical-resistance measurements. Moreover, we show how multiple repetitions of the RF experiment with the La-Ce superhydride make it possible to detect the increase in $T_{\text{c}}$ over time up to $\approx$ 260-270 K. This finding indicates the possibility of reaching a critical $T_{\text{c}}$ around 0$^\circ$C in the La-based superhydrides.

cond-mat.supr-con↗
arXiv2026

Effect of subgrid-scale anisotropy on wall-modeled large-eddy simulation of turbulent flow with smooth-body separation

We examine the role of anisotropic subgrid-scale (SGS) stress in wall-modeled large-eddy simulation (WMLES) of flow over a spanwise-uniform Gaussian-shaped bump, with emphasis on predicting flow separation. The simulations show that eddy-viscosity-based SGS models often yield non-monotonic predictions of the mean separation bubble size on the leeward side under grid refinement, whereas models incorporating anisotropic SGS stress produce more consistent results. To identify where SGS anisotropy is most critical, we introduce anisotropic SGS stress in selected regions of the domain. The results reveal that the windward side, where a strong favorable pressure gradient (FPG) occurs, is crucial in determining downstream separation. Analysis of the Reynolds stress transport equation shows that fluctuations of anisotropic SGS stress modify SGS dissipation and diffusion in this region, thereby altering the Reynolds stress and the onset of separation. Examination of the mean streamwise momentum equation indicates that at coarse resolutions, the mean SGS shear stress dominates, and the differences between the eddy-viscosity-based and anisotropic models remain minor. With grid refinement, resolved Reynolds stresses increasingly govern the near-wall momentum transport, and the influence of SGS stress fluctuations grows as they determine the SGS dissipation and diffusion of Reynolds stresses. Component-wise analysis of the SGS stress tensor further shows that the improvement arises mainly from including significant normal stress contributions. An a priori study using filtered direct numerical simulation of turbulent Couette-Poiseuille flow confirms that wall-bounded turbulence under FPG is highly anisotropic and that anisotropic SGS models provide a more realistic SGS stress representation than eddy-viscosity-based models.

physics.flu-dyn↗
arXiv2026

Mechanical Origin of High-Temperature Thermal Stability in Platinum Oxides

Platinum oxides are vital catalysts, but their limited thermal stability hinders applications. Recent studies have uncovered a structural transition in two-dimensional platinum oxides that significantly enhances their thermal resilience by several hundred Kelvin. Herein, we demonstrate that this enhanced stability stems from the mechanical robustness of the elastic network at the atomic scale. Prior to the transition, an over-constrained lattice generates localized states of self-stress through an incommensurate Moiré pattern with the platinum substrate, reducing thermal endurance. After the transition, the oxide shifts to a mechanically flexible structure with balanced degrees of freedom and constraints. The isostatic network, together with the platinum substrate, forms a commensurate Moiré superlattice that relaxes elastic energy and enhances stability. These findings highlight the fundamental role of network connectivity in governing thermal stability, and provide a design principle for catalysts in extreme environments.

cond-mat.mtrl-sci↗
arXiv2026

AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society

Understanding human behavior and society is a central focus in social sciences, with the rise of generative social science marking a significant paradigmatic shift. By leveraging bottom-up simulations, it replaces costly and logistically challenging traditional experiments with scalable, replicable, and systematic computational approaches for studying complex social dynamics. Recent advances in large language models (LLMs) have further transformed this research paradigm, enabling the creation of human-like generative social agents and realistic simulacra of society. In this paper, we propose AgentSociety, a large-scale social simulator that integrates LLM-driven agents, a realistic societal environment, and a powerful large-scale simulation engine. Based on the proposed simulator, we generate social lives for over 10k agents, simulating their 5 million interactions both among agents and between agents and their environment. Furthermore, we explore the potential of AgentSociety as a testbed for computational social experiments, focusing on five key social issues: polarization, the spread of inflammatory messages, the effects of universal basic income policies, the impact of external shocks such as hurricanes, and urban sustainability. These five issues serve as valuable cases for assessing AgentSociety's support for typical research methods -- such as surveys, interviews, and interventions -- as well as for investigating the patterns, causes, and underlying mechanisms of social issues. The alignment between AgentSociety's outcomes and real-world experimental results not only demonstrates its ability to capture human behaviors and their underlying mechanisms, but also underscores its potential as an important platform for social scientists and policymakers.

cs.SI↗
arXiv2026

Smoothing the Landscape: Causal Structure Learning via Diffusion Denoising Objectives

Understanding causal dependencies in observational data is critical for informing decision-making. These relationships are often modeled as Bayesian Networks (BNs) and Directed Acyclic Graphs (DAGs). Existing methods, such as NOTEARS and DAG-GNN, often face issues with scalability and stability in high-dimensional data, especially when there is a feature-sample imbalance. Here, we show that the denoising score matching objective of diffusion models could smooth the gradients for faster, more stable convergence. We also propose an adaptive k-hop acyclicity constraint that improves runtime over existing solutions that require matrix inversion. We name this framework Denoising Diffusion Causal Discovery (DDCD). Unlike generative diffusion models, DDCD utilizes the reverse denoising process to infer a parameterized causal structure rather than to generate data. We demonstrate the competitive performance of DDCDs on synthetic benchmarking data. We also show that our methods are practically useful by conducting qualitative analyses on two real-world examples. Code is available at this url: https://github.com/haozhu233/ddcd.

cs.LG↗
arXiv2026

Superhydrides on the way to ambient pressure: weak localization and persistent X-ray photoconductivity in BaSiH$_{8}$

Reducing the stabilization pressure of superhydrides represents one of the most important challenges in hydrogen-saturated compound chemistry. Moving in this direction, we studied the Ba-Si-H system at 0-142 GPa using transport measurements, 1H nuclear magnetic resonance, single-crystal and powder X-ray diffraction in the temperature range of 4-317 K. We synthesized the previously predicted cubic BaSiH$_{8}$ at pressures of 18-31 GPa. Remarkably, we demonstrate that BaSiH$_8$ remains stable upon decompression to ambient conditions and can be recovered from the diamond anvil cell. Obtained Ba-Si polyhydrides exhibit metallic and superconducting properties ($\textit{T$_c$}$ = 9 K, $\textit{B$_{c2}$}$(0)=13-16 T) at 142 GPa. However, at pressures below 50 GPa, these hydrides behave as degenerate semiconductors (bandgap < 0.4 meV) or poor metals with weak electron localization, negative magnetoresistance, photovoltaic effect, and persistent photoconductivity in the X-ray and visible range. Our work demonstrates the high-pressure synthesis of Ba-Si polyhydrides that remain stable upon decompression to ambient conditions, overcoming a critical bottleneck in superhydride chemistry and establishing a foundation for practical applications in hydrogen storage.

cond-mat.supr-con↗
arXiv2026

Floquet odd-parity collinear magnets

Altermagnets (AMs), recently discovered unconventional magnets distinct from both ferro- and antiferromagnets, have rapidly emerged as a prominent research topic in condensed matter physics. AMs are characterized by alternating collinear magnetic moments with zero net magnetization in real space, and spin splittings with even-parity symmetry in momentum space. However, their counterparts exhibiting odd-parity spin splittings are generally thought to be absent in collinear magnets. Here, we show that such unconventional odd-parity magnets can be induced from collinear antiferromagnets by symmetry engineering. Remarkably, using effective model analysis within Floquet-theory framework, we demonstrate that circularly polarized light irradiation of conventional antiferromagnetic lattices breaks a spin-preserving pseudo-time-reversal symmetry and induces both $p$- and $f$-wave magnets, realizing novel magnetic states dubbed Floquet odd-parity collinear magnets. Moreover, we also uncover light-induced antiferromagnetic Chern insulating states in the $f$-wave magnets. The proposed Floquet odd-parity magnet is confirmed by first-principles calculations of MnPSe$_{3}$ under circularly polarized light. Our work not only proposes a new class of unconventional magnets, but also opens an avenue for light-induced magnetic phenomena in spintronic applications.

cond-mat.mes-hall↗
arXiv2026

Phonon Echo from Multi-Level Systems and Many-Body Interactions in Low-Temperature Glasses

At low temperatures, glasses exhibit distinctive properties compared to crystalline solids. A notable example is the phonon echo, a phenomenon that motivated the two-level-system (TLS) model. This model has successfully explained many universal anomalies in glasses. Here, we extend the TLS framework to a multi-level system and show that phonon echoes persist when nonlinear energy structures and disorder are included. By incorporating virtual phonon exchange, we introduce many-body interactions between these multi-level systems, leading to nonlinear eigen-energies that enhance the echo signal. Meanwhile, finite-temperature thermal fluctuations cause dephasing, resulting in a decay of echo amplitude over time. The analytical and numerical results are consistent across semi-classical and quantum regimes. Our work validates the multi-level-system model and underscores the role of many-body interactions in low-temperature glassy dynamics.

cond-mat.dis-nn↗
arXiv2026

Radio-Frequency Gasket for Studies of Superconductivity in Diamond Anvil Cells

This work presents the development and testing of a novel radio-frequency (RF) gasket with a Lenz lens surface geometry for contactless measurements in diamond anvil cells (DACs). Conventional RF approaches, which fabricate the Lenz lens onto the diamond anvil itself, preclude the placement of electrical circuits. Our method overcomes this limitation by transferring the RF sensor to a composite Ta-based gasket. The sensor consists of single-turn microcoils that are formed by magnetron sputtering a gold film onto an insulating Ta$_2$O$_5$ layer. The Lenz lens topology is then patterned using focused ion beam etching. We validated this technique using polycrystalline Cu1234 and Bi2212 high-$\textit{T$_c$}$ superconductors at ambient and high pressures. The measurements consistently identified the superconducting transition temperature across carrier frequencies from 111 kHz to 200 MHz. This new gasket technique establishes a reliable and sensitive tool for contactless studies of superconductivity under high pressure.

cond-mat.supr-con↗
arXiv2025

Regional Resource Management for Service Provisioning in LEO Satellite Networks: A Topology Feature-Based DRL Approach

Satellite networks with wide coverage are considered natural extensions to terrestrial networks for their long-distance end-to-end (E2E) service provisioning. However, the inherent topology dynamics of low earth orbit satellite networks and the uncertain network scales bring an inevitable requirement that resource chains for E2E service provisioning must be efficiently re-planned. Therefore, achieving highly adaptive resource management is of great significance in practical deployment applications. This paper first designs a regional resource management (RRM) mode and further formulates the RRM problem that can provide a unified decision space independent of the network scale. Subsequently, leveraging the RRM mode and deep reinforcement learning framework, we develop a topology feature-based dynamic and adaptive resource management algorithm to combat the varying network scales. The proposed algorithm successfully takes into account the fixed output dimension of the neural network and the changing resource chains for E2E service provisioning. The matched design of the service orientation information and phased reward function effectively improves the service performance of the algorithm under the RRM mode. The numerical results demonstrate that the proposed algorithm with the best convergence performance and fastest convergence rate significantly improves service performance for varying network scales, with gains over compared algorithms of more than 2.7%, 11.9%, and 10.2%, respectively.

cs.NI↗
arXiv2025

Stability and Superconductivity of Ternary Polyhydrides

We review five years of experimental and theoretical attempts (2020-2025) to enhance the superconducting critical temperature ($\textit{T$_c$}$) of hydrogen-rich compounds by alloying binary superhydrides with additional elements. Despite predictions of higher $\textit{T$_c$}$ in ternary systems such as La-Y-H, La-Ce-H, and Ca-Mg-H, experiments consistently show that the maximum $\textit{T$_c$}$ in disordered ternary superhydrides does not exceed that of the best binary parent hydrides within experimental uncertainty. Instead, alloying primarily stabilizes high-symmetry polyhydride phases at lower pressures, enabling $\textit{T$_c$}$ = 200 K near 110-120 GPa, while also improving vortex pinning and upper critical fields. Magnetic dopants suppress $\textit{T$_c$}$, whereas nonmagnetic additives leave it nearly unchanged, reminiscent of Anderson's theorem. These findings indicate that alloying is unlikely to raise $\textit{T$_c$}$, but can reduce the pressures required to stabilize high-$\textit{T$_c$}$ phases. We propose that fully ordered ternary hydrides, synthesized via controlled hydrogenation of intermetallic precursors, offer a promising route toward this goal. One of the most promising compounds of this kind is the recently discovered LaSc$_2$H$_{24}$.

cond-mat.supr-con↗
arXiv2025

ReXGroundingCT: A 3D Chest CT Dataset for Segmentation of Findings from Free-Text Reports

We introduce ReXGroundingCT, the first publicly available dataset linking free-text findings to pixel-level 3D segmentations in chest CT scans. The dataset includes 3,142 non-contrast chest CT scans paired with standardized radiology reports from CT-RATE. Construction followed a structured three-stage pipeline. First, GPT-4 was used to extract and standardize findings, descriptors, and metadata from reports originally written in Turkish and machine-translated into English. Second, GPT-4o-mini categorized each finding into a hierarchical ontology of lung and pleural abnormalities. Third, 3D annotations were produced for all CT volumes: the training set was quality-assured by board-certified radiologists, and the validation and test sets were fully annotated by board-certified radiologists. Additionally, a complementary chain-of-thought dataset was created to provide step-by-step hierarchical anatomical reasoning for localizing findings within the CT volume, using GPT-4o and localization coordinates derived from organ segmentation models. ReXGroundingCT contains 16,301 annotated entities across 8,028 text-to-3D-segmentation pairs, covering diverse radiological patterns from 3,142 non-contrast CT scans. About 79% of findings are focal abnormalities and 21% are non-focal. The dataset includes a public validation set of 50 cases and a private test set of 100 cases, both annotated by board-certified radiologists. The dataset establishes a foundation for enabling free-text finding segmentation and grounded radiology report generation in CT imaging. Model performance on the private test set is hosted on a public leaderboard at https://rexrank.ai/ReXGroundingCT. The dataset is available at https://huggingface.co/datasets/rajpurkarlab/ReXGroundingCT.

eess.IV↗