In the past several years, magnetic topological materials have attracted interests since the exotic physical phenomena unveiled on these platforms. Using first-principles calculations, we predict that two new-type magnetic van der Waals crystals, EuBi4Te7 and EuSb4Te7, are topologically nontrivial with multiple topological phases in various magnetic configurations. In their magnetic ground states, coexisting antiferromagnetic topological insulator phase and axion insulator phase are identified. Therefore, massless Dirac fermion dispersions appear on the surfaces parallel to the out-of-plane orientation. When magnetized to ferromagnetic states, axion insulator phases protected by parity symmetry survive. Meanwhile, if the spins align along the x direction, they are mirror topological crystalline insulators with massless Dirac cones on their (001) and (010) surfaces. The magnetic easy axes of EuBi4Te7 and EuSb4Te7 are along the in-plane and out-of-plane orientations, respectively. These findings open more opportunities for the research and application of magnetic topological physics and topological quantum phase transitions.

The open-shell character of Kekulé graphene nanoflakes (GNFs) is conventionally rationalized by the gain of Clar aromatic π-sextets upon electron unpairing. While this rule successfully explains many quinoidal diradicaloids, it treats only the maximum number of sextets and neglects the multiplicity and spatial distribution of resonance configurations that realize the same Clar count. Here, we identify a second route to open-shell stabilization in which the maximum Clar-sextet number remains unchanged while the number of accessible Clar resonators increases substantially. We term this mechanism Clar-number-invariant resonance-space expansion. By enumerating closed-shell and open-shell Clar resonators and combining this analysis with a bonding entropy model (BEM), we show that electron unpairing can release closed-shell pairing constraints, enlarge the resonance manifold, and redistribute C–C bond occupancies away from localized single- and double-bond limits. The BEM-predicted number and spatial distribution of unpaired electrons correlate strongly with density-functional-theory diradical character, local magnetic moments, optimized C–C bond lengths, and relative energies across a broad set of GNFs. The resulting framework offers a graph-based and physically transparent route for screening open-shell carbon nanostructures and for designing tunable molecular spins without requiring an increase in the maximum Clar number.

Altermagnets – newly identified collinear antiferromagnets – carry zero net moment with non-relativistic, spin-polarized bands, distilling the best of ferromagnets and antiferromagnets into a single spintronic platform. Shrunking to the two-dimensional limit, they inherit the tunability of two-dimensional crystals while adding symmetry-protected spin splitting, a combination now driving intense experimental interest. Here, we review the symmetry classification of two-dimensional altermagnets based on spin-group theory and survey the growing list of candidate materials, emphasizing those with large spin splitting for experimental realization. We then examine strategies for engineering two-dimensional altermagnetism. This Review aims to consolidate theoretically proposed candidate materials and realization strategies for two-dimensional altermagnets, providing insights for future experimental efforts in this emerging field.

The growing coexistence of automated vehicles (AVs) and human-driven vehicles (HVs) in mixed traffic fundamentally alters car-following dynamics and elevates rear-end conflict risk. However, human drivers exhibit marked heterogeneity in driving styles-conservative, normal, or aggressive-which significantly moderates how kinematic factors translate into conflict risk. Ignoring such style‑dependent heterogeneity leads to biased risk analysis and ineffective countermeasures. Moreover, existing conflict prediction models typically output deterministic point estimates without quantifying predictive uncertainty, a critical shortcoming for safety‑critical automated driving decisions. To address these gaps, this study proposes a two‑stage, driving‑style‑stratified framework that jointly analyzes conflict risk mechanisms and delivers uncertainty‑aware predictions. Using the Lyft Level-5 autonomous driving dataset, we extract microscopic driving volatility indicators, quantify conflict risk via the Rear-end Conflict Risk Index (RCRI), and identify three driving styles through K-medoids clustering. In Stage 1, a Random Parameters Logit model with Heterogeneity in Means and Variances (RPLHMV) uncovers key risk factors and their multi-layered heterogeneity across AV‑following‑HV (AV‑HV) and HV‑following‑AV (HV‑AV) scenarios and driving styles. In Stage 2, informed by these insights, a Random Deep & Cross Network with Monte Carlo dropout (RDCN-MC) explicitly models behavioral heterogeneity and quantifies predictive uncertainty. Results show the significance and marginal effects of risk factors vary markedly across driving styles, with aggressive drivers exhibiting the strongest influences. The RDCN-MC model achieves accuracy, precision, recall, and F1-score above 97 %, 87 %, 93 %, and 90 % under class imbalance, consistently outperforming benchmarks. Each prediction is accompanied by an uncertainty estimate that effectively discriminates correct from incorrect predictions, serving as a confidence signal. Based on these findings, we propose differentiated, style-specific safety strategies for trustworthy automated driving control and human driving, thereby supporting proactive safety management in mixed traffic.

The unique electron deficiency of boron makes it challenging to determine the stable structures, leading to a wide variety of forms. In this work, we introduce a statistical model based on grand canonical ensemble theory that incorporates the octet rule to determine electron density in boron systems. This parameter-free model, referred to as the bonding free energy (BFE) model, aligns well with first-principles calculations and accurately predicts total energies. For borane clusters, the model successfully predicts isomer energies, hydrogen diffusion pathways, and optimal charge quantity for closo-boranes. In all-boron clusters, the absence of B-H bond constraints enables increased electron delocalization and flexibility. The BFE model systematically explains the geometric structures and chemical bonding in boron clusters, revealing variations in electron density that clarify their structural diversity. For borophene, the BFE model predicts that hexagonal vacancy distributions are influenced by bonding entropy, with uniform electron density enhancing stability. Notably, our model predicts borophenes with a vacancy concentration of 1 6 to exhibit increased stability with long-range periodicity. Therefore, the BFE model serves as a practical criterion for structure prediction, providing essential insights into the stability and physical properties of boron-based systems.

The modification of two-dimensional (2D) materials with metal atoms can lead to additional structures and properties; however, a systematic classification of metal-2D material interactions is still lacking. We propose a general framework for classifying these interactions based on two primary modes: metal atom adsorption on the surface and intercalation between layers. This framework, validated by first-principles calculations on representative van der Waals layered and nonlayered 2D materials interacting with transition metals (TMs) and alkali metals, provides a comprehensive explanation for experimental observations across various systems. Furthermore, the classification serves as a guide for designing stable and functional 2D materials. Our investigation of nonlayered borophene shows that interlayer-intercalated TM borides (TMBs) exhibit significantly higher stability than intralayer-doped TMBs. Physical property calculations further reveal that these sandwich-type TMBs possess superior mechanical stability and additional electronic properties, underscoring their potential for future applications.

Graphene antidot lattices (GALs) have garnered significant attention for their potential in semiconductor applications, yet the origin of bandgap opening remains controversial. Combining the octet rule, we propose a low-parameter physical model with weighted information entropy to quantitatively determine the electron density distribution, and the tight-binding parameters are obtained from the occupancy numbers based on the maximum entropy method. The results from our model reveal a complex bandgap opening mechanism in zigzag-edged hexagonal GALs (ZH-GALs), where specific inter-ribbon connections and quantum confinement cause the localization of π-electrons between antidots, leading to the elimination of energy levels degeneracy. We also observe that the anisotropy of rectangular ZH-GALs is enhanced as the defect radius increases, indicating a transition from GALs-like to graphene nanoribbons-like bandgap behavior. This study tells us that more than 1/9 ZH-GALs have considerable bandgaps, addressing the deficiency in band structure engineering between regimes dominated by defect scattering and quantum confinement.

Developing low-cost, high-performance metal-free oxygen reduction catalysts demands precise quantification of structure-performance relationships in nitrogen-doped carbons. Structural search algorithms combined with density functional theory (DFT) enable comprehensive analysis of catalytic performance versus structural features. Using nitrogen-doped graphene quantum dots (NGQDs) as models, we computationally resolve how nitrogen species types (pyridinic-N, graphitic-N) and substitution positions influence the stability and intrinsic activity. Boltzmann statistics quantify the contributions of all configurations to the current density during oxygen reduction. Furthermore, we identify dominant configurations grouping NGQDs into configuration lumps. Thermodynamically, nitrogen atoms preferentially occupy carbon atoms at defect-adjacent sites as pyridinic-N. Their spatial distribution controls local atomic charge redistribution and adsorption environments, thereby modulating intrinsic activity. These materials exhibit extreme configuration sensitivity: thermodynamically stable dominant configurations may contribute minimally to current density. Crucially, lumped pyridinic-N configurations dominate ORR performance. This work provides theoretical insights supporting carbon adjacent to pyridinic-N as the primary active site in N-doped carbon ORR catalysts. It establishes a universal framework for analyzing structure-activity relationships in nonmodel catalytic systems.

The Ising model is famous in condensed matter and statistical physics. In this work we present a free-fermion formulation of the two-dimensional classical Ising models on honeycomb, triangular and Kagomé lattices. Each Ising model is studied in the cases of a zero field and of an imaginary field i(π/2)kBT. We employ the decorated lattice technique, star-triangle transformation, and weak-graph expansion method to exactly map each Ising model in both cases into an eight-vertex model on the square lattice. The resulting vertex weights are shown to satisfy the free-fermion condition. In the zero-field case, each Ising model is an even free-fermion model. In the case of the imaginary field, the Ising model on the honeycomb lattice is an even free-fermion model, while the models on the triangular and Kagomé lattices are odd free-fermion models. We obtain the exact solution of the Kagomé lattice Ising model under the imaginary field i(π/2)kBT, a result not previously reported in the literature. We also show that the frustrated Ising models on the triangular and Kagomé lattices in the imaginary field still exhibit a non-zero residual entropy.

Travelers often adapt their behaviors to adverse weather, but existing research commonly assumes independent adaptive behaviors. This study employs a correlated random parameters bivariate probit model with heterogeneity in means (CRPBPHM) to jointly analyze intercity travelers' simultaneous adjustments of departure dates and travel modes in response to adverse weather events. Empirical data are derived from stated preference surveys, which integrate adverse weather scenarios into intercity travelers' latest actual travel behaviors within the Beijing-Tianjin-Hebei urban agglomeration. The results reveal a positive correlation between adjustments to departure dates and intercity travel modes. Intercity travelers' adaptive behaviors exhibit multi-layer heterogeneity. Four variables-access time, departure city, monthly income, and education level-are identified as random parameter variables, with departure city and education level showing mean heterogeneity. The correlations between these random parameter variables further influence adaptive behaviors. Additionally, influencing factors and their interactions distinctly shape adaptive decisions. Key findings indicate that intercity travelers are more likely to adjust departure dates than travel modes in adverse weather. Snowy and windy conditions lead to more frequent plan modifications compared to foggy or rainy weather. Rain prompts travelers with visiting purposes or longer stays to adjust departure dates. Train users are less likely to change departure dates in foggy weather and are less likely to switch travel modes during snowfall. These findings enhance our understanding of intercity travelers' joint adaptive behaviors in adverse weather, providing valuable insights for precise and effective emergency management and ensuring secure and smooth intercity transport operations.

Mixed-anion perovskite materials exhibit tunable properties, such as band gaps, stability, and charge transport, by modulating the composition and arrangement of the anions. This tunability enables a wide range of applications in fields such as optoelectronics, catalysis, and energy storage. The structural diversity enriches the material properties and enhances performance; however, it also poses a significant challenge in determining stable structures. The stability of such alloy materials depends not only on the elemental composition but also on the arrangement of X/Y elements within the lattice. To improve the understanding of the structure-property relationship in these alloy systems, a thorough exploration of the vast compositional and configurational space is essential. Herein, we integrate the cluster expansion (CE) method with the atom classification model (ACM) to efficiently pre-screen candidate structures and identify stable configurations. By considering the arrangement of anions, which exhibit short-range ordering within octahedra and randomness between octahedra, we have designed correlation functions for the ACM to reasonably reflect these characteristics. This approach enabled us to identify configurations of BaTa( O 1- x N x ) 3 , RbPb( F 1- x O x ) 3 and CsPb( Br 1- x Cl x ) 3 with higher stabilities without significantly increasing computational costs.

Graphene nanoflakes (GNFs) exhibit rich magnetic behaviors arising from two primary mechanisms: geometry frustration in non-Kekul & eacute; structures and electron delocalization-driven aromatic stabilization in Kekul & eacute;-type systems. Herein, we develop a unified bonding entropy model (BEM) to quantitatively characterize the magnetic properties in GNFs within a statistical framework, providing an entropy-based criterion for understanding and predicting bond occupancy numbers and unpaired electron distributions. While non-Kekul & eacute; systems naturally favor high-spin configurations due to topological frustration, the BEM reveals that even Kekul & eacute;-type GNFs can exhibit magnetic character when the entropy gain from unpaired electrons outweighs the loss of aromatic stabilization. The model predictions show excellent agreement with density functional theory calculations in terms of spin density distributions and unpaired electron counts. Our results establish bonding entropy as a general guiding principle for designing carbon-based magnetic materials with tunable magnetic properties.

We present a general and interpretable adatom model that enables the prediction and understanding of stable surface morphologies of nonmetallic elements deposited on metal substrates. By calculating the formation energies of isolated adatoms on various metal surfaces, we reveal the competition between interfacial interactions and the self-aggregation tendencies of the deposited elements. Based on this model, we classify four distinct surface morphologies that arise from nonmetal-metal substrate combinations. First-principles calculations across 15 nonmetallic elements and nine close-packed metal substrates show strong agreement between model predictions and experimentally reported morphologies. The model also identifies inconsistencies in certain experimentally observed structures and predicts previously unexplored stable morphologies, offering valuable guidance for future studies. Furthermore, we propose substrate engineering strategies, such as surface alloying, to modulate interfacial interactions, thereby enabling the controlled epitaxial growth of targeted two-dimensional materials, as supported by experimental validation.

Altermagnetism, as an unconventional antiferromagnetism, exhibits collinear-compensated magnetic order in real space and spin-splitting band structure in reciprocal space. In this work, we propose a general approach to generating multicomponent structures with two-dimensional (2D) altermagnetism, based on symmetry analysis. Specifically, by analyzing the space group of the crystal structures and their subgroups, we systematically categorize equivalent atomic positions and arrange them into orbits based on symmetry operations. Chemical elements are then allowed to occupy all atomic positions on these orbits, generating candidate structures with specific symmetries. We present a general technique for generating collinear-compensated magnetic order, characterized by the symmetrical interconnection between opposite-spin sublattices, and employ first-principles calculations to determine magnetic ground states of multicomponent materials. This approach integrates symmetry analysis with the screening of altermagnetic configurations to evaluate the likelihood of candidates possessing altermagnetism. To verify the methodology, we provide examples of previously unreported 2D altermagnets, such as Cr2Si2S3Se3, Fe2P2S3Se3, and V2O2BrI3, and evaluate their dynamical stability by calculating the phonon spectrum. The results demonstrate the feasibility of our approach in generating stable multicomponent structures with two-dimensional altermagnetism. Our research has significantly enriched the candidate materials for 2D altermagnets, and provides a reference for experimental synthesis.

Low-temperature expansion of Ising model has long been a topic of significant interest in condensed matter and statistical physics. In this paper we present new results of the coefficients in the low-temperature series of the Ising partition function on the square lattice, in the cases of a zero field and of an imaginary field i(pi /2)kBT . The coefficients in the low-temperature series of the free energy in the thermodynamic limit are represented using the explicit expression of the density function of the Fisher zeros. The asymptotic behavior of the sequence of the coefficients when the order goes to infinity is determined exactly, for both the series of the free energy and of the partition function. Our analytic and numerical results demonstrate that the convergence radius of the sequence is dependent on the accumulation points of the Fisher zeros, which have the smallest modulus. In the zero field case this accumulation point is the physical critical point, while in the imaginary field case it corresponds to a nonphysical singularity. We further discuss the relation between the series coefficients and the energy state degeneracies, using the combinatorial expression of the coefficients and the subgraph expansion.

With the rapid development of Automated Vehicle (AV) technologies, a mixed traffic environment comprising AVs and Human-Driven Vehicles (HVs) is expected to persist over the long term. While current research primarily focuses on the characteristics of car-following behavior between AVs and HVs, studies addressing conflict risk in these behaviors remain relatively limited. Using the Waymo Open Dataset for autonomous driving, this study empirically evaluates various advanced random parameter frameworks to investigate the influencing factors of conflict risk in three different car-following scenarios: AV-HV, HV-AV, and HV-HV, within the mixed traffic environment. Specifically, the Rear-end Collision Risk Index (RCRI) is defined as the binary outcome variable based on longitudinal car-following behavior data, with explanatory variables including kinematic variables of the preceding and following vehicles, inter-vehicle interactions, and environmental variables. The study primarily examines the suitability of model selection, explores unobserved multilayer heterogeneity, and compares the key factors influencing conflict risk across various car-following scenarios. The results indicate that in the three car-following scenarios-AV-HV, HV-AV, and HV-HV-the optimal models are the random parameters multinomial logit model (RPL), the random parameters multinomial logit model with heterogeneity in means and variances (RPLHMV), and the correlated random parameters multinomial logit model with heterogeneity in means (CRPLHM), respectively. The estimates derived from these optimal models reveal the random parameters, their heterogeneity in means and variances, and the potential correlations among the factors influencing conflict risks. This effectively captures the complex interactions between multiple factors, thereby reducing estimation biases. Furthermore, the significant factors and their respective magnitudes of impact on conflict risks vary across the three car-following scenarios. These variations across different car-following behaviors can enhance the accuracy of behavioral modeling and micro-simulation in mixed traffic flow, and support the formulation of differentiated traffic safety improvement measures.

Energy degeneracy in physical systems may be induced by symmetries of the Hamiltonian, and the resonance of degeneracy states in carbon nanostructures can effectively enhance the stability of the system. Combining the octet rule, we introduce a statistical model to determine the physical properties by lifting the energy degeneracy in carbon nanostructures. This model offers a direct path to accurately ascertain electron density distributions in quantum systems, akin to how charge density is used in density functional theory to deduce system properties. Our methodology diverges from traditional quantum mechanics, focusing instead on this unique statistical model by maximizing bonding entropy to determine the fundamental properties of materials. Applied to carbon nanoclusters and graphynes, our model not only precisely predicts bonding energies and electron density without relying on external parameters but also enhances the prediction of electronic structures through bond occupancy numbers, which act as effective hopping integrals. This innovation offers insights into the structural properties and quantum behavior of electrons across various dimensions.

The rapid growth of the number of vehicles and the inadaptability of signal control have become major factors restricting traffic efficiency and people’s travel experience. Optimizing traffic signal timing can alleviate congestion and reduce delays, but accurately predicting traffic flow for the next signal cycle remains a complex challenge. To address this, this paper proposes an analytical signal control optimization algorithm that integrates prediction and coordination at urban regional intersections to improve traffic efficiency. First, an intelligent learning scheme is designed, embedding real datasets into the Long Short-Term Memory (LSTM) network to predict traffic condition information in subsequent signal queues. Then, an objective optimization model is established based on kinematic wave theory and flow-density diagram. This model seeks the globally optimal signal timing solution by dynamically adjusting signal timing, cycle length, and phase splits. For traffic congestion scenarios, the phase configuration of multi-phase intersections is improved to enhance green light time utilization and traffic capacity. Taking the traffic operation scenario of Songwei South Road in Shanghai as an example, the simulation study verifies the performance of the proposed strategy.

Mitigating traffic injury rate plays an essential role in sustainable urban development and is closely related to public health and human well-being. The inequity of traffic injury rate undermines equitable access to transportation infrastructure and poses a significant threat to the safety of residents during their commutes. Although previous studies have examined the association between socio-demographic characteristics and regional traffic crash risk, they seldom consider the spatial heterogeneity of the traffic injury rate inequity especially for the vulnerable groups. This study tackles three main challenges in quantifying spatial inequity in traffic injury rate, including identifying appropriate spatial units for macro-level crash modelling, integrating network topology measures, and examining the inequities suffered by vulnerable group. The global-scale spatial lag model (SLM) and local-scale geographically weighted regression (GWR) were employed at the census tract, Voronoi diagram and grid cell levels in New York City. The results highlight significant spatial variations in social vulnerability, showing that vulnerable indicators of housing cost burden, older age, minority, and those living in mobile homes are linked to increased traffic injury rate, especially in urban core regions. Furthermore, network topology measures indicates that increased network complexity and the buildings density would increase traffic injury rate. The traffic injury rate inequity level of each region was evaluated to identify areas with significant inequities for prioritization in future traffic planning. Recommendations like subsidized traffic insurance rates, enhanced public transportation services, and educational campaigns tailored for vulnerable groups are proposed. By prioritizing the needs of vulnerable groups and addressing the structural factors contributing to traffic injuries, policymakers can create safer and more equitable urban environment.