arXiv subjects

Jiabin Xie

Publications and source records attributed to Jiabin Xie.

9 recordsLinked to original sources

arXiv2026

Real-space Visualization of Emergent Electron Crystals in Rhombohedral Graphene

Strongly interacting electrons can spontaneously break spatial symmetries to form electron crystals, exemplified by the Wigner crystal. Recent studies of topological flat bands in rhombohedral graphene have suggested more exotic forms of crystallization, including anomalous Hall crystals that entangle charge order with nontrivial topology and metallic electron crystals in which localized and itinerant carriers coexist. Direct real-space observation of these states, however, has remained elusive. Here we use scanning tunneling microscopy and spectroscopy to visualize emergent electron crystals in rhombohedral hexalayer graphene. At low electric fields and over a finite range of hole doping, we observe electronic lattice patterns that evolve from honeycomb to oblique order through a first-order quantum phase transition with increasing hole density. The Fermi surface extracted from quasiparticle-interference measurements lacks the geometry needed to account for these patterns through conventional nesting. Together with metallic transport and a crystal-site density much lower than the doped carrier density, this supports metallic electron crystals in which a subset of carriers crystallizes. The honeycomb crystal occupies the same phase space as the multiferroic orbital magnetism observed previously in transport and exhibits domain stabilization by a small magnetic field, which may suggest a possible metallic anomalous Hall crystal. With increasing magnetic field, the oblique phase develops a $\sqrt{2}\times\sqrt{2}$ reconstruction with a crystal-sublattice energy splitting that increases linearly with field, corresponding to a $g$-factor of 16. This may reflect an orbital-antiferromagnetic electron crystal with alternating orbital magnetization across the lattice. These results establish a new paradigm of electron crystallization in which charge order is intertwined with orbital magnetism.

cond-mat.mes-hall↗

arXiv2026

Competing Chern states revealed by quasiparticle charging in moiré rhombohedral graphene

Moiré materials realize a versatile platform for exploring the physics of fractional Chern insulators (FCIs). The recently observed evolution from FCIs to an extended quantum anomalous Hall background upon lowering the electronic temperature in moiré rhombohedral graphene (mRG)8 raises a fundamental question: Is it caused by a failure to equilibrate the edge states of an FCI or by a genuine phase transition in the bulk from an FCI to a generalized anomalous Hall crystal? Here we address this question by probing quasiparticle charging in a mesoscopic mRG antidot device and by bulk resistance measurements, both of which are bulk-sensitive and free from complications from edge states. Tunneling to the mRG antidot reveals quasiparticles carrying one electron charge for both Chern states at filling factors ν=1 and 2/3 at low temperatures. Temperature dependence measurements of the bulk resistance near ν=2/3 further suggest a thermodynamic phase transition from an FCI to a generalized anomalous Hall crystal at temperatures below about 150mK. The results clearly exclude the edge state equilibration scenario and favor the phase transition scenario. Our work establishes mesoscopic probes as a powerful approach to uncover competing ground states in moiré materials and provides a basis for probing fractionalized excitations in FCIs.

cond-mat.mes-hall↗

arXiv2026

Large Language Model based Interactive Decision-Making for Autonomous Driving

In high-conflict mixed-traffic scenarios involving human-driven and autonomous vehicles, most existing autonomous driving systems default to overly conservative behaviors, lack proactive interaction, and consequently suffer from limited public acceptance. To mitigate intent misunderstandings and decision failures, we present a Large Language Model based interactive decision-making framework that augments scene understanding and intent-aware interaction to jointly improve safety and efficiency. The approach uses Object-Process Methodology to semantically model complex multi-vehicle scenes, abstracting low-level perceptual data into objects, processes, and relations, thereby streamlining reasoning over latent causal structure. Building on this representation, the Large Language Model parses both explicit and implicit intents of surrounding agents and, under jointly enforced safety and efficiency constraints, selects candidate maneuvers. We further generate perturbed trajectory candidates via Monte Carlo sampling and evaluate them to obtain an optimized executable trajectory. To foster transparency and coordination with nearby road users, the final decision is translated by the Large Language Model into concise natural-language messages and broadcast through an external Human-Machine Interface, completing a closed loop from scene understanding to action to language. Experiments in a cluster driving simulator demonstrate that the proposed method outperforms traditional baselines across safety, comfort, and efficiency metrics, while a Turing-test-style evaluation indicates a high degree of human-likeness in decision making. Besides, these results suggest that coupling semantic scene abstraction with Large Language Model mediated intent reasoning and language-based eHMI communication offers a practical pathway toward interactive, trustworthy autonomous driving in dense mixed traffic.

cs.RO↗

arXiv2026

POLAR-PIC: A Holistic Framework for Matrixized PIC with Co-Designed Compute, Layout, and Communication

Particle-in-Cell (PIC) simulations are fundamental to plasma physics but often suffer from limited scalability due to particle-grid interaction bottlenecks and particle redistribution costs. Specifically, the particle-grid interaction computations have not taken full advantage of the emerging Matrix Processing Units (MPUs), the particle motion introduces irregular memory accesses, and the bulk-synchronous redistribution further destroys long-term data locality thereby limiting parallel efficiency. To address these inefficiencies, we present POLAR-PIC, a co-designed framework for large-scale PIC simulations that (i) reformulates Field Interpolation into an MPU-friendly outer-product form, (ii) maintains a physically ordered particle layout to preserve memory contiguity, and (iii) overlaps particle communication with Deposition to hide redistribution overhead. The evaluation on the pilot system of an Exascale supercomputer demonstrates that POLAR-PIC accelerates the entire particle-processing phase by up to 10.9x in uniform plasma and 4.4x in real-world laser-ion acceleration scenarios compared to the native WarpX reference pipeline on LX2. Ablation studies reveal that the speedups achieved by Interpolation and Deposition are 8.0x and 13.2x, respectively, and the asynchronous communication design sustains a 99.1% overlap ratio. In cross-platform comparisons, POLAR-PIC achieves 13.2% of theoretical peak efficiency on the CPU-based LS system, while WarpX reaches 9.6% on NVIDIA A800 GPUs. Notably, the scalability evaluation demonstrates that POLAR-PIC maintains 67.5% weak scaling efficiency on over 2 million cores under high-migration dynamic workloads, highlighting the importance of holistic co-design for future matrix-centric HPC systems.

cs.DC↗

arXiv2026

Magnetic-field-induced superconductivity in hexalayer rhombohedral graphene

In conventional superconductors, superconductivity is generally suppressed by external magnetic fields due to spin-singlet pairing. Here, we report signatures of in-plane-magnetic-field-induced superconductivity in hexalayer rhombohedral graphene and reveal electric-field control of its depairing behavior. With the application of a small in-plane magnetic field $B_{\parallel}$, a superconducting state emerges within a narrow band along a phase boundary. Its properties evolve continuously with increasing $B_{\parallel}$: the superconducting region progressively shifts toward higher electric field as the $B_{\parallel}$ increases and the transition temperature rises with increasing $B_{\parallel}$. Remarkably, the superconducting state remains robust under $B_{\parallel}$ up to 14 T, far exceeding the conventional Pauli limit. Quantum oscillation measurements further reveal that the superconductivity emerges from nematic Fermi surface reconstruction. These results suggest a spin-polarized superconducting states with unconventional origins.

cond-mat.mes-hall↗

arXiv2026

Superconductivity and Ferroelectric Orbital Magnetism in Semimetallic Rhombohedral Hexalayer Graphene

Rhombohedral multilayer graphene has emerged as a promising platform for exploring correlated and topological quantum phases, enabled by its Berry-curvature-bearing flat bands. While prior work has focused on separated conduction and valence bands, we probe the extensive semimetallic regime of rhombohedral hexalayer graphene. We survey a rich phase diagram dominated by flavor-symmetry breaking and reveal an electric-field-driven band inversion by fermiology. Near this inversion, we observe two superconducting-like states confined to regions with coexisting electron and hole Fermi surfaces, suggesting a possible dual-carrier origin. In addition, we identify a ferroelectric orbital magnet that undergoes sharp switching under unipolar electric fields, signaling spontaneous electric polarization. Unlike previously reported multiferroicity near zero electric field, this new state exhibits magnetic hysteresis reversible by electric field, consistent with a phenomenological model of coupled electric and magnetic polarization. Our work elucidates the correlated semimetal regime in rhombohedral graphene and underscores its potential to host diverse quantum phases.

cond-mat.str-el↗

arXiv2026

Matrix-PIC: Harnessing Matrix Outer-product for High-Performance Particle-in-Cell Simulations

Particle-in-Cell (PIC) simulations spend most of their execution time on particle--grid interactions, where fine-grained atomic updates become a major bottleneck on traditional many-core CPUs. Recent CPU architectures integrate specialized Matrix Processing Units (MPUs) that efficiently support matrix outer-product operations, offering new opportunities to overcome this limitation. Leveraging this architectural shift, this work focuses on redesigning the current deposition step of PIC simulations under a matrix-centric execution model. We present MatrixPIC, the first holistic co-design of the deposition kernel, data layout, and incremental particle sorting tailored to the hybrid MPU--VPU SIMD model on modern CPUs. MatrixPIC introduces: (i)~a block-matrix formulation of the current deposition algorithm that maps naturally to MPU outer-product primitives; (ii)~a hybrid execution pipeline that combines MPU-based high-density accumulation with VPU-based data preparation and control flow; and (iii)~an $O(1)$-amortized incremental sorter based on a gapped packed-memory array to preserve data locality for efficient MPU execution. Evaluated on a next-generation HPC platform, MatrixPIC achieves significant performance gains. In Laser-Wakefield Acceleration (LWFA) simulations, it delivers up to $2.63\times$ speedup in total runtime. For third-order deposition, the core kernel is accelerated by $8.7\times$ over the baseline and $2.0\times$ over the best hand-optimized VPU implementation. Moreover, MatrixPIC reaches $83.08\%$ of theoretical CPU peak performance, nearly $2.8\times$ higher than a highly optimized CUDA kernel on a data center GPU. These results demonstrate the effectiveness of matrix-oriented co-design for accelerating PIC simulations on emerging CPU architectures.

cs.DC↗

arXiv2024

UNR: Unified Notifiable RMA Library for HPC

Remote Memory Access (RMA) enables direct access to remote memory to achieve high performance for HPC applications. However, most modern parallel programming models lack schemes for the remote process to detect the completion of RMA operations. Many previous works have proposed programming models and extensions to notify the communication peer, but they did not solve the multi-NIC aggregation, portability, hardware-software co-design, and usability problems. In this work, we proposed a Unified Notifiable RMA (UNR) library for HPC to address these challenges. In addition, we demonstrate the best practice of utilizing UNR within a real-world scientific application, PowerLLEL. We deployed UNR across four HPC systems, each with a different interconnect. The results show that PowerLLEL powered by UNR achieves up to a 36% acceleration on 1728 nodes of the Tianhe-Xingyi supercomputing system.

cs.DC↗

arXiv2021

A low-communication-overhead parallel method for the 3D incompressible Navier-Stokes equations

This paper presents a low-communication-overhead parallel method for solving the 3D incompressible Navier-Stokes equations. A fully-explicit projection method with second-order space-time accuracy is adopted. Combined with fast Fourier transforms, the parallel diagonal dominant (PDD) algorithm for the tridiagonal system is employed to solve the pressure Poisson equation, differing from its recent applications to compact scheme derivatives computation (Abide et al. 2017) and alternating-direction-implicit method (Moon et al. 2020). The number of all-to-all communications is decreased to only two, in a 2D pencil-like domain decomposition. The resulting MPI/OpenMP hybrid parallel code shows excellent strong scalability up to $10^4$ cores and small wall-clock time per timestep. Numerical simulations of turbulent channel flow at different friction Reynolds numbers ($Re_τ$ = 550, 1000, 2000) have been conducted and the statistics are in good agreement with the reference data. The proposed method allows massively simulation of wall turbulence at high Reynolds numbers as well as many other incompressible flows.

physics.flu-dyn↗