arXiv subjects

Zichong Zhang

Publications and source records attributed to Zichong Zhang.

3 recordsLinked to original sources

arXiv2026

Real-Symmetric Hamiltonian Enables Near-Linear Scaling for Fast Million-Atom Electronic Structure Computations

The exploration of quantum phenomena in mesoscale materials, such as moire superlattices, is limited by the cubic scaling cost of conventional electronic structure methods. Here, we introduce a scalable tight binding framework that achieves near linear scaling, enabling mesoscopic quantum simulations. By transforming the complex Hermitian Bloch Hamiltonian into an equivalent real symmetric form, the method avoids dense diagonalization by combining sparse LDL decomposition with Sylvester's law of inertia for spectral slicing and global rank calibration. This formulation enables efficient band structure calculations for large scale systems, solving magic angle twisted bilayer graphene in minutes on a standard laptop and extending to 1.5 million atoms within days on a single workstation. Applying this framework to ultra low twist angle structures with atomistic strain relaxation, we find robust isolated low-energy band clusters over several finite ultra low angle windows down to 0.09 degree. Our framework provides an efficient computational platform for studying quantum materials at experimentally relevant length scales and supports data driven discovery in large scale moire systems.

physics.comp-ph↗

arXiv2025

Identifying eclipsing binary stars with TESS data based on a new hybrid deep learning model

Eclipsing binary systems (EBs), as foundational objects in stellar astrophysics, have garnered significant attention in recent years. These systems exhibit periodic decreases in light intensity when one star obscures the other from the observer's perspective, producing characteristic light curves (LCs). With the advent of the Transiting Exoplanet Survey Satellite (TESS), a vast repository of stellar LCs has become available, offering unprecedented opportunities for discovering new EBs. To efficiently identify such systems, we propose a novel method that combines LC data and generalized Lomb-Scargle periodograms (GLS) data to classify EBs. At the core of this method is CNN Attention LSTM Net (CALNet), a hybrid deep learning model integrating Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and an Attention Mechanism based on the Convolutional Block Attention Module (CBAM). We collected 4,225 EB samples, utilizing their 2-minute cadence LCs for model training and validation. CALNet achieved a recall rate of 99.1%, demonstrating its robustness and effectiveness. Applying it to TESS 2-minute LCs from Sectors 1 to 74, we identified 9,351 new EBs after manual visual inspection, significantly expanding the known sample size. This work highlights the potential of advanced deep-learning techniques in large-scale astronomical surveys and provides a valuable resource for further studies on EBs.

astro-ph.SR↗

arXiv2025

Programming frictionless interfaces for moiré layers

Structural superlubricity in van der Waals layered systems holds immense promise for diverse nanoscale contacts devices and energy-efficient applications. While all-direction structural superlubricity has been widely investigated, the understanding towards the more fundamental directional structural superlubricity requires further attentions. In this study, we reveal the physical origins of directional structural superlubricity, which reduces to all-direction superlubricity under certain conditions. By investigating the evolution of incomplete moiré tiles at crystalline interfaces, our general scaling approaches establish the mapping from geometry to tunable directional superlubricity, agreeing with large scale molecular dynamics simulations at both homogeneous or heterogeneous interfaces. Furthermore, diverse programmable frictionless motions of nanoflakes traveling inside double-surface nanoconfinement systems can be achieved. Our work delivers new insights into the design of ultra-low frictional interfaces for future nanoscale tribology and nanoconfinement transport.

cond-mat.mes-hall↗