arXiv subjects

Zeyu Li

Publications and source records attributed to Zeyu Li.

At least 91 records · Page 5Linked to original sources

arXiv2023

A unique van Hove singularity in kagome superconductor CsV$_{3-x}$Ta$_x$Sb$_5$ with enhanced superconductivity

Van Hove singularity (VHS) has been considered as a driving source for unconventional superconductivity. A VHS in two-dimensional (2D) materials consists of a saddle point connecting electron-like and hole-like bands. In a rare case, when a VHS appears at Fermi level, both electron-like and hole-like conduction can coexist, giving rise to an enhanced density of states as well as an attractive component of Coulomb interaction for unconventional electronic pairing. However, this van Hove scenario is often destroyed by an incorrect chemical potential or competing instabilities. Here, by using angle-resolved photoemission measurements, we report the observation of a VHS perfectly aligned with the Fermi level in a kagome superconductor CsV$_{3-x}$Ta$_x$Sb$_5$ (x~0.4), in which a record-high superconducting transition temperature is achieved among all the current variants of AV$_3$Sb$_5$ (A=Cs, Rb, K) at ambient pressure. Doping dependent measurements reveal the important role of van Hove scenario in boosting superconductivity, and spectroscopic-imaging scanning tunneling microscopy measurements indicate a distinct superconducting state in this system.

cond-mat.supr-con↗

arXiv2023

Sensing orbital hybridization of graphene-diamond interface with a single spin

Interfacial interactions are crucial in a variety of fields and can greatly affect the electric, magnetic, and chemical properties of materials. Among them, interface orbital hybridization plays a fundamental role in the properties of surface electrons such as dispersion, interaction, and ground states. Conventional measurements of electronic states at interfaces such as scanning tunneling microscopes are all based on electric interactions which, however, suffer from strong perturbation on these electrons. Here we unveil a new experimental detection of interface electrons based on the weak magnetic interactions between them and the nitrogen-vacancy (NV) center in diamond. With negligible perturbation on the interface electrons, their physical properties can be revealed by the NV spin coherence time. In our system, the interface interaction leads to significant decreases in both the density and coherence time of the electron spins at the diamond-graphene interface. Furthermore, together with electron spin resonance spectra and first-principle calculations, we can retrieve the effect of interface electron orbital hybridization. Our study opens a new pathway toward the microscopic probing of interfacial electronic states with weak magnetic interactions and provides a new avenue for future research on material interfaces.

quant-ph↗

arXiv2023

DeePMD-kit v2: A software package for Deep Potential models

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments.

physics.chem-ph↗

arXiv2023

CluCDD:Contrastive Dialogue Disentanglement via Clustering

A huge number of multi-participant dialogues happen online every day, which leads to difficulty in understanding the nature of dialogue dynamics for both humans and machines. Dialogue disentanglement aims at separating an entangled dialogue into detached sessions, thus increasing the readability of long disordered dialogue. Previous studies mainly focus on message-pair classification and clustering in two-step methods, which cannot guarantee the whole clustering performance in a dialogue. To address this challenge, we propose a simple yet effective model named CluCDD, which aggregates utterances by contrastive learning. More specifically, our model pulls utterances in the same session together and pushes away utterances in different ones. Then a clustering method is adopted to generate predicted clustering labels. Comprehensive experiments conducted on the Movie Dialogue dataset and IRC dataset demonstrate that our model achieves a new state-of-the-art result.

cs.CL↗

arXiv2023

Unidirectional electron-phonon coupling as a "fingerprint'' of the nematic state in a kagome superconductor

Electronic nematicity has been commonly observed in juxtaposition with unconventional superconductivity. Understanding the nature of the nematic state, as well as its consequence on the electronic band structure and superconductivity, has become a pivotal focus in condensed matter physics. Here we use spectroscopic imaging-scanning tunneling microscopy to visualize how the interacting quasiparticles organize themselves in the nematic state of kagome superconductor CsV$_{3-x}$Ti$_x$Sb$_5$, in which twofold symmetric (C$_2$) quasiparticle scattering interference of the vanadium kagome bands emerges below the bulk nematic transition temperature (T$_{nem}$). Surprisingly, we find that the coupling to collective modes, i.e., the phonon, dramatically alters the electrons self-energy and renormalizes the Fermi velocity of the in-plane vanadium d$_{xy/x^2-y^2}$ bands only along the C$_2$ direction, making the low-energy dispersion and electron dynamics highly nonequivalent along the three lattice directions. The anti-correlation between T$_{nem}$ and the superconducting transition temperature upon Ti substitution further suggests a possible competition between superconductivity and electron nematicity in this series, with a principal superconducting gap opening on the same V bands once the nematic state is totally suppressed. The organizing principle of these quasiparticles provides essential information for understanding the interplay between charge density wave and superconductivity in these kagome superconductors, and also reveals a previously unexplored form that expands the landscape for modelling electronic nematicity in systems where electron correlations and lattice degree of freedom act in concert.

cond-mat.supr-con↗

arXiv2023

Vertex-Critical $(P_5, chair)$-Free Graphs

Given two graphs $H_1$ and $H_2$, a graph $G$ is $(H_1,H_2)$-free if it contains no induced subgraph isomorphic to $H_1$ or $H_2$. A $P_t$ is the path on $t$ vertices. A chair is a $P_4$ with an additional vertex adjacent to one of the middle vertices of the $P_4$. A graph $G$ is $k$-vertex-critical if $G$ has chromatic number $k$ but every proper induced subgraph of $G$ has chromatic number less than $k$. In this paper, we prove that there are finitely many 5-vertex-critical $(P_5,chair)$-free graphs.

math.CO↗

arXiv2022

SemanticAxis: Exploring Multi-attribute Data by Semantics Construction and Ranking Analysis

Mining the distribution of features and sorting items by combined attributes are two common tasks in exploring and understanding multi-attribute (or multivariate) data. Up to now, few have pointed out the possibility of merging these two tasks into a united exploration context and the potential benefits of doing so. In this paper, we present SemanticAxis, a technique that achieves this goal by enabling analysts to build a semantic vector in two-dimensional space interactively. Essentially, the semantic vector is a linear combination of the original attributes. It can be used to represent and explain abstract concepts implied in local (outliers, clusters) or global (general pattern) features of reduced space, as well as serving as a ranking metric for its defined concepts. In order to validate the significance of combining the above two tasks in multi-attribute data analysis, we design and implement a visual analysis system, in which several interactive components cooperate with SemanticAxis seamlessly and expand its capacity to handle complex scenarios. We prove the effectiveness of our system and the SemanticAxis technique via two practical cases.

cs.DB↗

arXiv2022

Dual Space Coupling Model Guided Overlap-Free Scatterplot

The overdraw problem of scatterplots seriously interferes with the visual tasks. Existing methods, such as data sampling, node dispersion, subspace mapping, and visual abstraction, cannot guarantee the correspondence and consistency between the data points that reflect the intrinsic original data distribution and the corresponding visual units that reveal the presented data distribution, thus failing to obtain an overlap-free scatterplot with unbiased and lossless data distribution. A dual space coupling model is proposed in this paper to represent the complex bilateral relationship between data space and visual space theoretically and analytically. Under the guidance of the model, an overlap-free scatterplot method is developed through integration of the following: a geometry-based data transformation algorithm, namely DistributionTranscriptor; an efficient spatial mutual exclusion guided view transformation algorithm, namely PolarPacking; an overlap-free oriented visual encoding configuration model and a radius adjustment tool, namely $f_{r_{draw}}$. Our method can ensure complete and accurate information transfer between the two spaces, maintaining consistency between the newly created scatterplot and the original data distribution on global and local features. Quantitative evaluation proves our remarkable progress on computational efficiency compared with the state-of-the-art methods. Three applications involving pattern enhancement, interaction improvement, and overdraw mitigation of trajectory visualization demonstrate the broad prospects of our method.

cs.GR↗

arXiv2022

Color-Kinematics Duality for Sudakov Form Factor in Non-Supersymmetric Pure Yang-Mills Theory

We study the duality between color and kinematics for the Sudakov form factors of ${\rm tr}(F^2)$ in non-supersymmetric pure Yang-Mills theory. We construct the integrands that manifest the color-kinematics duality up to two loops. The resulting numerators are given in terms of Lorentz products of momenta and polarization vectors, which have the same powers of loop momenta as that from the Feynman rules. The integrands are checked by $d$-dimensional unitarity cuts and are valid in any dimension. We find that massless-bubble and tadpole topologies are needed at two loops to realize the color-kinematics duality. Interestingly, the two-loop solution contains a large number of free parameters suggesting the duality may hold at higher loop orders.

hep-th↗

arXiv2022

Chern Number Tunable Quantum Anomalous Hall Effect in Monolayer Transitional Metal Oxides via Manipulating Magnetization Orientation

Although much effort has been made to explore quantum anomalous Hall effect (QAHE) in both theory and experiment, the QAHE systems with tunable Chern numbers are yet limited. Here, we theoretically propose that NiAsO$_3$ and PdSbO$_3$, monolayer transitional metal oxides, can realize QAHE with tunable Chern numbers via manipulating their magnetization orientations. When the magnetization lies in the \textit{x-y} plane and all mirror symmetries are broken, the low-Chern-number (i.e., $\mathcal{C}=\pm1$) phase emerges. When the magnetization exhibits non-zero \textit{z}-direction component, the system enters the high-Chern-number (i.e., $\mathcal{C}=\pm3$) phase, even in the presence of canted magnetization. The global band gap can approach the room-temperature energy scale in monolayer PdSbO$_3$ (23.4 meV), when the magnetization is aligned to \textit{z}-direction. By using Wannier-based tight-binding model, we establish the phase diagram of magnetization induced topological phase transition. Our work provides a high-temperature QAHE system with tunable Chern number for the practical electronic application.

cond-mat.mes-hall↗

arXiv2022

Large Rashba Spin-Orbit Coupling and High-Temperature Quantum Anomalous Hall Effect in Re-Intercalated Graphene/CrI$_3$ Heterostructure

In 2010, quantum anomalous Hall effect (QAHE) in graphene was proposed in the presence of Rashba spin-orbit coupling and ferromagnetic exchange field. After a decade's experimental exploration, the anomalous Hall conductance can only reach about 0.25 in the units of $2e^2/h$, which was attributed to the tiny Rashba spin-orbit coupling. Here, we theoretically show that Re-intercalation in graphene/CrI$_3$ heterostructure can not only induce sizeable Rashba spin-orbit coupling ($>$ 40~meV), but also open up large band gaps at valleys $K$ (22.2 meV) and $K' $ (30.3 meV), and a global band gap over 5.5 meV (19.5 meV with random Re distribution) hosting QAHE. A low-energy continuum model is constructed to explain the underlying physical mechanism. We find that Rashba spin-orbit coupling is robust against external stress whereas a tensile strain can increase the global bulk gap. Furthermore, we also show that Re-intercalated graphene with hexagonal boron-nitride can also realize QAHE with bulk gap over 40~meV, indicating the tunability of $5d$-intercalated graphene-based heterostructure. Our finding makes a great leap towards the experimental realization of graphene-based QAHE, and will definitely accelerate the practical application of graphene-based low-power electronics.

cond-mat.mes-hall↗

arXiv2022

Pressure-induced dimensional crossover in a kagome superconductor

The recently discovered kagome superconductors AV3Sb5 exhibit tantalizing high-pressure phase diagrams, in which a new dome-like superconducting phase emerges under moderate pressure. However, its origin is as yet unknown. Here, we carried out the high-pressure electrical measurements up to 150 GPa, together with the high-pressure X-ray diffraction measurements and first-principles calculations on CsV3Sb5. We find the new superconducting phase to be rather robust and inherently linked to the interlayer Sb2-Sb2 interactions. The formation of Sb2-Sb2 bonds at high pressure tunes the system from two-dimensional to three-dimensional and pushes the Pz orbital of Sb2 upward across the Fermi level, resulting in enhanced density of states and increase of TC. Our work demonstrates that the dimensional crossover at high pressure can induce a topological phase transition and is related to the abnormal high-pressure TC evolution. Our findings should apply for other layered materials.

cond-mat.supr-con↗

arXiv2021

Realizing Kagome Band Structure in Two-Dimensional Kagome Surface States of $RV_{6}Sn_{6}$ ($R$=Gd,Ho)

We report angle resolved photoemission experiments on a newly discovered family of kagome metals $RV_{6}Sn_{6}$ ($R$=Gd, Ho). Intrinsic bulk states and surface states of the vanadium kagome layer are differentiated from those of other atomic sublattices by the real-space resolution of the measurements with a small beam spot. Characteristic Dirac cone, saddle point and flat bands of the kagome lattice are observed. Our results establish the two-dimensional (2D) kagome surface states as a new platform to investigate the intrinsic kagome physics.

cond-mat.mtrl-sci↗

arXiv2021

Powering Comparative Classification with Sentiment Analysis via Domain Adaptive Knowledge Transfer

We study Comparative Preference Classification (CPC) which aims at predicting whether a preference comparison exists between two entities in a given sentence and, if so, which entity is preferred over the other. High-quality CPC models can significantly benefit applications such as comparative question answering and review-based recommendations. Among the existing approaches, non-deep learning methods suffer from inferior performances. The state-of-the-art graph neural network-based ED-GAT (Ma et al., 2020) only considers syntactic information while ignoring the critical semantic relations and the sentiments to the compared entities. We proposed sentiment Analysis Enhanced COmparative Network (SAECON) which improves CPC ac-curacy with a sentiment analyzer that learns sentiments to individual entities via domain adaptive knowledge transfer. Experiments on the CompSent-19 (Panchenko et al., 2019) dataset present a significant improvement on the F1 scores over the best existing CPC approaches.

cs.CL↗

arXiv2021

Recommend for a Reason: Unlocking the Power of Unsupervised Aspect-Sentiment Co-Extraction

Compliments and concerns in reviews are valuable for understanding users' shopping interests and their opinions with respect to specific aspects of certain items. Existing review-based recommenders favor large and complex language encoders that can only learn latent and uninterpretable text representations. They lack explicit user attention and item property modeling, which however could provide valuable information beyond the ability to recommend items. Therefore, we propose a tightly coupled two-stage approach, including an Aspect-Sentiment Pair Extractor (ASPE) and an Attention-Property-aware Rating Estimator (APRE). Unsupervised ASPE mines Aspect-Sentiment pairs (AS-pairs) and APRE predicts ratings using AS-pairs as concrete aspect-level evidence. Extensive experiments on seven real-world Amazon Review Datasets demonstrate that ASPE can effectively extract AS-pairs which enable APRE to deliver superior accuracy over the leading baselines.

cs.IR↗

arXiv2021

Towards Visual Explainable Active Learning for Zero-Shot Classification

Zero-shot classification is a promising paradigm to solve an applicable problem when the training classes and test classes are disjoint. Achieving this usually needs experts to externalize their domain knowledge by manually specifying a class-attribute matrix to define which classes have which attributes. Designing a suitable class-attribute matrix is the key to the subsequent procedure, but this design process is tedious and trial-and-error with no guidance. This paper proposes a visual explainable active learning approach with its design and implementation called semantic navigator to solve the above problems. This approach promotes human-AI teaming with four actions (ask, explain, recommend, respond) in each interaction loop. The machine asks contrastive questions to guide humans in the thinking process of attributes. A novel visualization called semantic map explains the current status of the machine. Therefore analysts can better understand why the machine misclassifies objects. Moreover, the machine recommends the labels of classes for each attribute to ease the labeling burden. Finally, humans can steer the model by modifying the labels interactively, and the machine adjusts its recommendations. The visual explainable active learning approach improves humans' efficiency of building zero-shot classification models interactively, compared with the method without guidance. We justify our results with user studies using the standard benchmarks for zero-shot classification.

cs.HC↗

arXiv2021

Large-Gap Quantum Anomalous Hall Effect in Monolayer Halide Perovskite

We theoretically propose a family of structurally stable monolayer halide perovskite A$_3$B$_2$C$_9$ (A=Rb, Cs; B=Pd, Pt; C=Cl, Br) with easy magnetization planes. These materials are all half-metals with large spin gaps over 1~eV accompanying with a single spin Dirac point located at K point. When the spin-orbit coupling is switched on, we further show that Rb$_3$Pt$_2$Cl$_9$, Cs$_3$Pd$_2$Cl$_9$, and Cs$_3$Pt$_2$Cl$_9$ monolayers can open up large band gaps from 63 to 103 meV to harbor quantum anomalous Hall effect with Chern numbers of $\mathcal{C}=\pm1$, whenever the mirror symmetry is broken by the in-plane magnetization. The corresponding Berezinskii-Kosterlitz-Thouless transition temperatures are over 248~K. Our findings provide a potentially realizable platform to explore quantum anomalous Hall effect and spintronics at high temperatures.

cond-mat.mes-hall↗

arXiv2019

Quantum Anomalous Hall Effect by Coupling Heavy Atomic Layers with CrI$_{3}$

We explored the possibility of realizing quantum anomalous Hall effect by placing heavy-element atomic layer on top of monolayer CrI$_{3}$ with a natural cleavage surface and broken time-reversal symmetry. We showed that CrI$_{3}$/X (X = Bi, Sb, or As) systems can open up a sizable bulk gap to harbour quantum anomalous Hall effect, e.g., CrI$_{3}$/Bi is a natural magnetic insulator with a bulk gap of 30~meV, which can be further enlarged via strain engineering or adjusting spin orientations. We also found that the ferromagnetic properties (magnetic anisotropic energy and Curie temperature) of pristine CrI$_{3}$ can be further improved due to the presence of heavy atomic layers, and the spin orientation can be utilized as a useful knob to tune the band structure and Fermi level of CrI$_{3}$/Bi system. The topological nature, together with the enhanced ferromagnetism, can unlock new potential applications for CrI$_{3}$-based materials in spintronics and electronics.

cond-mat.mtrl-sci↗