arXiv subjects

Yunzhe Liu

Publications and source records attributed to Yunzhe Liu.

12 recordsLinked to original sources

arXiv2026

Ideal Topological Flat Bands in Two-dimensional Moiré Heterostructures with Type-II Band Alignment

Topological flat bands play an essential role in inducing exotic interacting physics, ranging from fractional Chern insulators to superconductivity, in moiré materials. In this work, we propose a design principle for realizing topological flat bands with "ideal quantum geometry", namely the trace of Fubini-Study metric equals to the Berry curvature, in a class of two-dimensional moiré heterostructures with type-II band alignment. We first introduce a moiré Chern-band model to describe this system and show that topological flat bands can be realized in this model when the moiré superlattice potential is stronger than the type-II atomic band gap of the heterostructure. Next, we map this model into a topological heavy fermion model that consists of a localized orbital for "f-electron" and a conducting band for "c-electron". We find that both the flatness and quantum geometry of the flat band in the topological heavy fermion model depend on the energy gap between c-electron and f-electron bands at $Γ$ which is experimentally controllable via external gate voltages. This tunability will allow us to realize an ideal topological flat band with zero band-width and ideal quantum geometry. Our design strategy of topological flat bands is insensitive of twist angle. We also discuss possible material candidates for moiré heterostructures with type-II band alignment.

cond-mat.mes-hall↗

arXiv2025

QMBench: A Research Level Benchmark for Quantum Materials Research

We introduce QMBench, a comprehensive benchmark designed to evaluate the capability of large language model agents in quantum materials research. This specialized benchmark assesses the model's ability to apply condensed matter physics knowledge and computational techniques such as density functional theory to solve research problems in quantum materials science. QMBench encompasses different domains of the quantum material research, including structural properties, electronic properties, thermodynamic and other properties, symmetry principle and computational methodologies. By providing a standardized evaluation framework, QMBench aims to accelerate the development of an AI scientist capable of making creative contributions to quantum materials research. We expect QMBench to be developed and constantly improved by the research community.

cond-mat.mtrl-sci↗

arXiv2025

A region-specific brain dysfunction underlies cognitive impairment in long COVID brain fog

Long COVID "brain fog" is a common and debilitating subjective syndrome often associated with persistent cognitive impairment after COVID-19 infection. Here we identify a specific regional brain dysfunction that mediates this cognitive impairment and provide evidence that targeted neuromodulation improves this deficit. In 120 patients with long COVID brain fog, we found an aberrant perceptual processing pattern. Patients with more severe brain fog committed significantly more false alarms (impulsive responses to non-signals) despite preserved overall accuracy. Both high-density (128-channel) EEG and structural MRI analyses provided converging evidence of a right inferior insula deficit, characterized by a blunted neural monitoring signal and cortical atrophy. We confirmed this deficit in a separate 796-participant UK Biobank longitudinal COVID re-imaging cohort, where COVID-19 survivors also showed selective impairment on a perceptual processing task and corresponding longitudinal atrophy of the right inferior insula compared with healthy controls. Finally, in a proof-of-principle randomized, sham-controlled trial (n = 40), a non-invasive, excitatory theta-burst ultrasound stimulation protocol targeting the right inferior insula rescued the perceptual deficit by reducing false alarms. These findings provide evidence of a causal role for right inferior insula dysfunction in long COVID-related perceptual impairment and show that modulation of this region can rescue the deficit, establishing it as a novel therapeutic target for long COVID cognitive impairment.

q-bio.NC↗

arXiv2025

Symmetry-enforced Moiré Topology

Topological flat bands in two-dimensional (2D) moiré materials have emerged as promising platforms for exploring the interplay between topology and correlation effects. However, realistic calculations of moiré band topology using density functional theory (DFT) are computationally inefficient due to the large number of atoms in a single moiré unit cell. In this work, we propose a systematic scheme to predict the topology of moiré bands from atomic symmetry data and moiré symmetry group, both of which can be efficiently extracted from DFT. Specifically, for $Γ$-valley electron gases, we find that certain combinations of atomic symmetry data and moiré symmetry groups can enforce nontrivial band topology in the low-energy moiré bands, as long as the moiré band gap is smaller than the atomic band splitting at the moiré Brillouin zone boundary. This symmetry-enforced nontrivial moiré topology, including both topological insulators and topological semimetals, is robust against various material-specific details such as the precise form and strength of the moiré potential or the exact twist angle. By exhaustively scanning all 2D atomic symmetry data and moiré symmetry groups, we identify 197 combinations that can yield symmetry-enforced nontrivial moiré topology, and we verify one such combination using a moiré model with cubic Rashba spin-orbit coupling. By screening the existing 2D material database, we currently identify 92 monolayer materials with (i) the low-energy bands near $Γ$ and (ii) the atomic symmetry data that belong to those combinations. Our approach is generalizable to other valleys and provides a useful guideline for experimental efforts to discover and design new topologically nontrivial moiré materials.

cond-mat.mes-hall↗

arXiv2025

Orbital Hybridization-Induced Ising-Type Superconductivity in a Confined Gallium Layer

In low-dimensional superconductors, the interplay between quantum confinement and interfacial hybridization effects can reshape Cooper pair wavefunctions and induce novel forms of unconventional superconductivity. In this work, we employ a plasma-free, carbon buffer layer-assisted confinement epitaxy method to synthesize trilayer gallium (Ga) sandwiched between a graphene layer and a 6H-SiC(0001) substrate, forming an air-stable graphene/trilayer Ga/SiC heterostructure. In this confined light-element Ga layer, we demonstrate interfacial Ising-type superconductivity driven by atomic orbital hybridization between the Ga layer and the SiC substrate. Electrical transport measurements reveal that the in-plane upper critical magnetic field u0Hc2,|| reaches ~21.98T at T=400 mK, approximately 3.38 times the Pauli paramagnetic limit (~6.51T). Angle-resolved photoemission spectroscopy (ARPES) measurements combined with theoretical calculations confirm the presence of split Fermi surfaces with Ising-type spin textures at the K and K' valleys of the confined Ga layer strongly hybridized with SiC. Moreover, by incorporating finite relaxation time induced by impurity scattering into an Ising-type superconductivity model, we reproduce the entire temperature-dependent u0Hc2,|| phase diagram. This work establishes a new strategy to realize unconventional pairing wavefunctions by combining quantum confinement and interfacial hybridization effects in superconducting thin films. It also opens new avenues for designing scalable superconducting quantum electronic and spintronic devices through interfacial engineering.

cond-mat.mes-hall↗

arXiv2024

Cognitive maps and schizophrenia

Structured internal representations (cognitive maps) shape cognition, from imagining the future and counterfactual past, to transferring knowledge to new settings. Our understanding of how such representations are formed and maintained in biological and artificial neural networks has grown enormously. The cognitive mapping hypothesis of schizophrenia extends this enquiry to psychiatry, proposing that diverse symptoms - from delusions to conceptual disorganisation - stem from abnormalities in how the brain forms structured representations. These abnormalities may arise from a confluence of neurophysiological perturbations (excitation-inhibition imbalance, resulting in attractor instability and impaired representational capacity), and/or environmental factors such as early life psychosocial stressors (which impinge on representation learning). This proposal thus links knowledge of neural circuit abnormalities, environmental risk factors, and symptoms.

q-bio.NC↗

arXiv2024

Engineering Miniband Topology via Band-Folding in Moiré Superlattice Materials

The emergence of topologically non-trivial flat bands in moiré materials provides an opportunity to explore the interplay between topological physics and correlation effects, leading to the recent experimental realization of interacting topological phases, e.g. fractional Chern insulators. In this work, we propose a mechanism of band inversion induced by band-folding from the moiré superlattice potential for engineering topological minibands in moiré materials. We illustrate this mechanism via two classes of model Hamiltonians, namely the Rashba model and the Bernevig-Hughes-Zhang (BHZ) model, under the moiré superlattice potentials. Moiré minibands with non-trivial band topology, including Z2 number, mirror Chern number and fragile topology, have been found and the topological phase diagram is constructed for these moiré models. A general theory based on band representations in the morié Brillouin zone is also developed for a generalization of this mechanism to other space groups. Possible experimental realizations of our model Hamiltonian are discussed.

cond-mat.mes-hall↗

arXiv2022

Replay and compositional computation

Replay in the brain has been viewed as rehearsal, or, more recently, as sampling from a transition model. Here, we propose a new hypothesis: that replay is able to implement a form of compositional computation where entities are assembled into relationally-bound structures to derive qualitatively new knowledge. This idea builds on recent advances in neuroscience which indicate that the hippocampus flexibly binds objects to generalizable roles and that replay strings these role-bound objects into compound statements. We suggest experiments to test our hypothesis, and we end by noting the implications for AI systems which lack the human ability to radically generalize past experience to solve new problems.

q-bio.NC↗

arXiv2022

Calibration Strategy of the JUNO-TAO Experiment

The Taishan Antineutrino Observatory (JUNO-TAO, or TAO) is a satellite detector for the Jiangmen Underground Neutrino Observatory (JUNO). Located near the Taishan reactor, TAO independently measures the reactor's antineutrino energy spectrum with unprecedented energy resolution. To achieve this goal, energy response must be well calibrated. Using the Automated Calibration Unit (ACU) and the Cable Loop System (CLS) of TAO, multiple radioactive sources are deployed to various positions in the detector to perform a precise calibration of energy response. The non-linear energy response can be controlled within 0.6% with different energy points of these radioactive sources. It can be further improved by using $^{12}\rm B$ decay signals produced by cosmic muons. Through the energy non-uniformity calibration, residual non-uniformity is less than 0.2%. The energy resolution degradation and energy bias caused by the residual non-uniformity can be controlled within 0.05% and 0.3%, respectively. In addition, the stability of other detector parameters, such as the gain of each silicon photo-multiplier, can be monitored with a special ultraviolet LED calibration system.

physics.ins-det↗

arXiv2022

Self-Conditioned Generative Adversarial Networks for Image Editing

Generative Adversarial Networks (GANs) are susceptible to bias, learned from either the unbalanced data, or through mode collapse. The networks focus on the core of the data distribution, leaving the tails - or the edges of the distribution - behind. We argue that this bias is responsible not only for fairness concerns, but that it plays a key role in the collapse of latent-traversal editing methods when deviating away from the distribution's core. Building on this observation, we outline a method for mitigating generative bias through a self-conditioning process, where distances in the latent-space of a pre-trained generator are used to provide initial labels for the data. By fine-tuning the generator on a re-sampled distribution drawn from these self-labeled data, we force the generator to better contend with rare semantic attributes and enable more realistic generation of these properties. We compare our models to a wide range of latent editing methods, and show that by alleviating the bias they achieve finer semantic control and better identity preservation through a wider range of transformations. Our code and models will be available at https://github.com/yzliu567/sc-gan

cs.CV↗

arXiv2022

Neural Implicit 3D Shapes from Single Images with Spatial Patterns

Neural implicit functions have achieved impressive results for reconstructing 3D shapes from single images. However, the image features for describing 3D point samplings of implicit functions are less effective when significant variations of occlusions, views, and appearances exist from the image. To better encode image features, we study a geometry-aware convolutional kernel to leverage geometric relationships of point samplings by the proposed \emph{spatial pattern}, i.e., a structured point set. Specifically, the kernel operates at 2D projections of 3D points from the spatial pattern. Supported by the spatial pattern, the 2D kernel encodes geometric information that is crucial for 3D reconstruction tasks, while traditional ones mainly consider appearance information. Furthermore, to enable the network to discover more adaptive spatial patterns for further capturing non-local contextual information, the kernel is devised to be deformable manipulated by a spatial pattern generator. Experimental results on both synthetic and real datasets demonstrate the superiority of the proposed method. Pre-trained models, codes, and data are available at https://github.com/yixin26/SVR-SP.

cs.CV↗

arXiv2021

Topological phases of the dimerized Hofstadter butterfly

In this work, we study the topological phases of the dimerized square lattice in the presence of an external magnetic field. The dimerization pattern in the lattice's hopping amplitudes can induce a series of bulk energy gap openings in the Hofstadter spectrum at certain fractional fillings, giving rise to various topological phases. In particular, we show that at $\frac{1}{2}$-filling the topological quadrupole insulator phase with a quadrupole moment quantized to $\frac{e}{2}$ and associated corner-localized mid-gap states exists in certain parameter regime for all magnetic fluxes. At $\frac{1}{4}$ filling, the system can host obstructed atomic limit phases or Chern insulator phases. For those configurations gapped at fillings below $\frac{1}{4}$, the system is in Chern insulator phases of various non-vanishing Chern numbers. Across the phase diagram, both bulk-obstructed and boundary-obstructed topological phase transitions exist in this model.

cond-mat.mes-hall↗