arXiv subjects

Lan Chen

Publications and source records attributed to Lan Chen.

At least 73 records · Page 4Linked to original sources

arXiv2023

Realization of a two-dimensional checkerboard lattice in monolayer Cu$_2$N

Two-dimensional checkerboard lattice, the simplest line-graph lattice, has been intensively studied as a toy model, while material design and synthesis remain elusive. Here, we report theoretical prediction and experimental realization of the checkerboard lattice in monolayer Cu$_2$N. Experimentally, monolayer Cu$_2$N can be realized in the well-known N/Cu(100) and N/Cu(111) systems that were previously mistakenly believed to be insulators. Combined angle-resolved photoemission spectroscopy measurements, first-principles calculations, and tight-binding analysis show that both systems host checkerboard-derived hole pockets near the Fermi level. In addition, monolayer Cu$_2$N has outstanding stability in air and organic solvents, which is crucial for further device applications.

cond-mat.mtrl-sci↗

arXiv2023

Bound excitons and bandgap engineering in violet phosphorus

Violet phosphorus (VP), the most stable phosphorus allotrope, is a van der Waals semiconductor that can be used to construct $\textit{p}$-type nanodevices. Recently, high-quality VP crystals have been synthesized while a deep insight into their excitonic properties and bandgap tailoring approaches, which are crucial for their optoelectronic device applications, is still lacking. Here, we study the optical properties of ultrathin VP by second harmonic generation, photoluminescence, and optical absorption spectroscopy. We observed strong bound exciton emission that is 0.48 eV away from the free exciton emission, which is among the largest in 2D materials. In addition, the bandgaps of VP are highly sensitive to the number of layers and external strain, which provides convenient approaches for bandgap engineering. The strong bound exciton emission and tunable bandgaps make VP a promising material in optoelectronic devices.

cond-mat.mtrl-sci↗

arXiv2023

Anisotropic magnetism and electronic properties of the kagome metal SmV6Sn6

Kagome magnets are expected to feature emergent properties due to the interplays among geometry, magnetism, electronic correlation, and band topology. The magnetism and topological electronic states can be tuned via the rare earth engineering in RV6Sn6 kagome metals, where R is a rare earth element. Herein, we present the synthesis and characterization of SmV6Sn6, a metal with two-dimensional kagome nets of vanadium and frustrated triangular Sm lattice. Partial of the Sm atoms are shifted from the normal R positions by c/2 along the c axis. Magnetic measurements reveal obvious anisotropy, where the easy magnetic axis is within the ab plane. Electronic transports show multiband behaviors below 200 K. Density functional theory calculations find that the electronic structure of SmV6Sn6 hosts flat bands, Dirac cone, and saddle point arising from the V-3d electrons near the Fermi level. No evidence for the existence of charge density wave or magnetic order down to 2 K can be observed. Thus, SmV6Sn6 can be viewed as a modest disordered derivative of the RV6Sn6 structure, in which the disordered rare earth ions can suppress the magnetic order and charge density wave in the RV6Sn6 kagome family.

cond-mat.str-el↗

arXiv2023

GCNet: Graph Completion Network for Incomplete Multimodal Learning in Conversation

Conversations have become a critical data format on social media platforms. Understanding conversation from emotion, content and other aspects also attracts increasing attention from researchers due to its widespread application in human-computer interaction. In real-world environments, we often encounter the problem of incomplete modalities, which has become a core issue of conversation understanding. To address this problem, researchers propose various methods. However, existing approaches are mainly designed for individual utterances rather than conversational data, which cannot fully exploit temporal and speaker information in conversations. To this end, we propose a novel framework for incomplete multimodal learning in conversations, called "Graph Complete Network (GCNet)", filling the gap of existing works. Our GCNet contains two well-designed graph neural network-based modules, "Speaker GNN" and "Temporal GNN", to capture temporal and speaker dependencies. To make full use of complete and incomplete data, we jointly optimize classification and reconstruction tasks in an end-to-end manner. To verify the effectiveness of our method, we conduct experiments on three benchmark conversational datasets. Experimental results demonstrate that our GCNet is superior to existing state-of-the-art approaches in incomplete multimodal learning. Code is available at https://github.com/zeroQiaoba/GCNet.

cs.LG↗

arXiv2022

Observation of Gapped Dirac Cones in a Two-Dimensional Su-Schrieffer-Heeger Lattice

The Su-Schrieffer-Heeger (SSH) model in a two-dimensional rectangular lattice features gapless or gapped Dirac cones with topological edge states along specific peripheries. While such a simple model has been recently realized in photonic/acoustic lattices and electric circuits, its material realization in condensed matter systems is still lacking. Here, we study the atomic and electronic structure of a rectangular Si lattice on Ag(001) by angle-resolved photoemission spectroscopy and theoretical calculations. We demonstrate that the Si lattice hosts gapped Dirac cones at the Brillouin zone corners. Our tight-binding analysis reveals that the Dirac bands can be described by a 2D SSH model with anisotropic polarizations. The gap of the Dirac cone is driven by alternative hopping amplitudes in one direction and staggered potential energies in the other one and hosts topological edge states. Our results establish an ideal platform to explore the rich physical properties of the 2D SSH model.

cond-mat.mtrl-sci↗

arXiv2022

Crystal and electronic structure of a quasi-two-dimensional semiconductor Mg$_3$Si$_2$Te$_6$

We report the synthesis and characterization of a Si-based ternary semiconductor Mg$_3$Si$_2$Te$_6$, which exhibits a quasi-two-dimensional structure, where the trigonal Mg$_2$Si$_2$Te$_6$ layers are separated by Mg ions. Ultraviolet-visible absorption spectroscopy and density functional theory calculations were performed to investigate the electronic structure. The experimentally determined direct band gap is 1.39 eV, consistent with the value of the density function theory calculations. Our results reveal that Mg$_3$Si$_2$Te$_6$ is a direct gap semiconductor with a relatively narrow gap, which is a potential candidate for infrared optoelectronic devices.

cond-mat.mtrl-sci↗

arXiv2022

Deep Learning-based Massive MIMO CSI Acquisition for 5G Evolution and 6G

Recently, inspired by successful applications in many fields, deep learning (DL) technologies for CSI acquisition have received considerable research interest from both academia and industry. Considering the practical feedback mechanism of 5th generation (5G) New radio (NR) networks, we propose two implementation schemes for artificial intelligence for CSI (AI4CSI), the DL-based receiver and end-to-end design, respectively. The proposed AI4CSI schemes were evaluated in 5G NR networks in terms of spectrum efficiency (SE), feedback overhead, and computational complexity, and compared with legacy schemes. To demonstrate whether these schemes can be used in real-life scenarios, both the modeled-based channel data and practically measured channels were used in our investigations. When DL-based CSI acquisition is applied to the receiver only, which has little air interface impact, it provides approximately 25\% SE gain at a moderate feedback overhead level. It is feasible to deploy it in current 5G networks during 5G evolutions. For the end-to-end DL-based CSI enhancements, the evaluations also demonstrated their additional performance gain on SE, which is 6% -- 26% compared with DL-based receivers and 33% -- 58% compared with legacy CSI schemes. Considering its large impact on air-interface design, it will be a candidate technology for 6th generation (6G) networks, in which an air interface designed by artificial intelligence can be used.

eess.SP↗

arXiv2022

Exfoliation of 2D van der Waals crystals in ultrahigh vacuum for interface engineering

Two-dimensional (2D) materials and their heterostructures have been intensively studied in recent years due to their potential applications in electronic, optoelectronic, and spintronic devices. Nonetheless, the realization of 2D heterostructures with atomically flat and clean interfaces remains challenging, especially for air-sensitive materials, which hinders the in-depth investigation of interface-induced phenomena and the fabrication of high-quality devices. Here, we circumvented this challenge by exfoliating 2D materials in an ultrahigh vacuum. Remarkably, ultraflat and clean substrate surfaces can assist the exfoliation of 2D materials, regardless of the substrate and 2D material, thus providing a universal method for the preparation of heterostructures with ideal interfaces. In addition, we studied the properties of two prototypical systems that cannot be achieved previously, including the electronic structure of monolayer phospherene and optical responses of transition metal dichalcogenides on different metal substrates. Our work paves the way to engineer rich interface-induced phenomena, such as proximity effects and moiré superlattices.

cond-mat.mtrl-sci↗

arXiv2022

Observation of topological flat bands in the kagome semiconductor Nb$_3$Cl$_8$

The destructive interference of wavefunctions in a kagome lattice can give rise to topological flat bands (TFBs) with a highly degenerate state of electrons. Recently, TFBs have been observed in several kagome metals, including Fe$_3$Sn$_2$, FeSn, CoSn, and YMn$_6$Sn$_6$. Nonetheless, kagome materials that are both exfoliable and semiconducting are lacking, which seriously hinders their device applications. Herein, we show that Nb$_3$Cl$_8$, which hosts a breathing kagome lattice, is gapped out because of the absence of inversion symmetry, while the TFBs survive because of the protection of the mirror reflection symmetry. By angle-resolved photoemission spectroscopy measurements and first-principles calculations, we directly observe the TFB and a moderate band gap in Nb$_3$Cl$_8$. By mechanical exfoliation, we successfully obtain monolayers of Nb$_3$Cl$_8$ and confirm that they are stable under ambient conditions. In addition, our calculations show that monolayers of Nb$_3$Cl$_8$ have a magnetic ground state, thus providing opportunities to study the interplay between geometry, topology, and magnetism.

cond-mat.mtrl-sci↗

arXiv2022

Atomic-scale Manipulation of Single-Polaron in a Two-Dimensional Semiconductor

Polaron is a composite quasiparticle derived from an excess carrier trapped by local lattice distortion, and it has been studied extensively for decades both theoretically and experimentally. However, atomic-scale creation and manipulation of single-polarons in real space have still not been achieved so far, which precludes the atomistic understanding of the properties of polarons as well as their applications. Herein, using scanning tunneling microscopy, we succeeded to create single polarons in a monolayer two-dimensional semiconductor, CoCl2. Combined with first-principles calculations, two stable polaron configurations, centered at on-top and hollow sites, respectively, have been revealed. Remarkably, a series of manipulation progresses, from creation, erasure to transition, can be accurately implemented on individual polarons. Our results pave the way to understand the polaronic physics at atomic level, and the easy control of single polarons in 2D semiconductor may open the door to 2D polaronics including the data storage.

cond-mat.mes-hall↗

arXiv2022

Observation of one-dimensional Dirac fermions in silicon nanoribbons

Dirac materials, which feature Dirac cones in the reciprocal space, have been one of the hottest topics in condensed matter physics in the past decade. To date, 2D and 3D Dirac Fermions have been extensively studied, while their 1D counterparts are rare. Recently, Si nanoribbons (SiNRs), which are composed of alternating pentagonal Si rings, have attracted intensive attention. However, the electronic structure and topological properties of SiNRs are still elusive. Here, by angle-resolved photoemission spectroscopy, scanning tunneling microscopy/spectroscopy measurements, first-principles calculations, and tight-binding model analysis, we demonstrate the existence of 1D Dirac Fermions in SiNRs. Our theoretical analysis shows that the Dirac cones derive from the armchairlike Si chain in the center of the nanoribbon and can be described by the Su-Schrieffer-Heeger model. These results establish SiNRs as a platform for studying the novel physical properties in 1D Dirac materials.

cond-mat.mtrl-sci↗

arXiv2021

Multi-feature super-resolution network for cloth wrinkle synthesis

Existing physical cloth simulators suffer from expensive computation and difficulties in tuning mechanical parameters to get desired wrinkling behaviors. Data-driven methods provide an alternative solution. It typically synthesizes cloth animation at a much lower computational cost, and also creates wrinkling effects that highly resemble the much controllable training data. In this paper we propose a deep learning based method for synthesizing cloth animation with high resolution meshes. To do this we first create a dataset for training: a pair of low and high resolution meshes are simulated and their motions are synchronized. As a result the two meshes exhibit similar large-scale deformation but different small wrinkles. Each simulated mesh pair are then converted into a pair of low and high resolution "images" (a 2D array of samples), with each sample can be interpreted as any of three features: the displacement, the normal and the velocity. With these image pairs, we design a multi-feature super-resolution (MFSR) network that jointly train an upsampling synthesizer for the three features. The MFSR architecture consists of two key components: a sharing module that takes multiple features as input to learn low-level representations from corresponding super-resolution tasks simultaneously; and task-specific modules focusing on various high-level semantics. Frame-to-frame consistency is well maintained thanks to the proposed kinematics-based loss function. Our method achieves realistic results at high frame rates: 12-14 times faster than traditional physical simulation. We demonstrate the performance of our method with various experimental scenes, including a dressed character with sophisticated collisions.

cs.GR↗

arXiv2021

Deep Deformation Detail Synthesis for Thin Shell Models

In physics-based cloth animation, rich folds and detailed wrinkles are achieved at the cost of expensive computational resources and huge labor tuning. Data-driven techniques make efforts to reduce the computation significantly by a database. One type of methods relies on human poses to synthesize fitted garments which cannot be applied to general cloth. Another type of methods adds details to the coarse meshes without such restrictions. However, existing works usually utilize coordinate-based representations which cannot cope with large-scale deformation, and requires dense vertex correspondences between coarse and fine meshes. Moreover, as such methods only add details, they require coarse meshes to be close to fine meshes, which can be either impossible, or require unrealistic constraints when generating fine meshes. To address these challenges, we develop a temporally and spatially as-consistent-as-possible deformation representation (named TS-ACAP) and a DeformTransformer network to learn the mapping from low-resolution meshes to detailed ones. This TS-ACAP representation is designed to ensure both spatial and temporal consistency for sequential large-scale deformations from cloth animations. With this representation, our DeformTransformer network first utilizes two mesh-based encoders to extract the coarse and fine features, respectively. To transduct the coarse features to the fine ones, we leverage the Transformer network that consists of frame-level attention mechanisms to ensure temporal coherence of the prediction. Experimental results show that our method is able to produce reliable and realistic animations in various datasets at high frame rates: 10 ~ 35 times faster than physics-based simulation, with superior detail synthesis abilities than existing methods.

cs.CV↗

arXiv2020

Observation of quantum spin Hall states in Ta$_2$Pd$_3$Te$_5$

Two-dimensional topological insulators (2DTIs), which host the quantum spin Hall (QSH) effect, are one of the key materials in next-generation spintronic devices. To date, experimental evidence of the QSH effect has only been observed in a few materials, and thus, the search for new 2DTIs is at the forefront of physical and materials science. Here, we report experimental evidence of a 2DTI in the van der Waals material Ta$_2$Pd$_3$Te$_5$. First-principles calculations show that each monolayer of Ta$_2$Pd$_3$Te$_5$ is a 2DTI with weak interlayer interactions. Combined transport, angle-resolved photoemission spectroscopy, and scanning tunneling microscopy measurements confirm the existence of a band gap at the Fermi level and topological edge states inside the gap. These results demonstrate that Ta$_2$Pd$_3$Te$_5$ is a promising material for fabricating spintronic devices based on the QSH effect.

cond-mat.mtrl-sci↗

arXiv2020

Experimental observation of Dirac cones in artificial graphene lattices

Artificial lattices provide a tunable platform to realize exotic quantum devices. A well-known example is artificial graphene (AG), in which electrons are confined in honeycomb lattices and behave as massless Dirac fermions. Recently, AG systems have been constructed by manipulating molecules using scanning tunnelling microscope tips, but the nanoscale size typical for these constructed systems are impossible for practical device applications and insufficient for direct investigation of the electronic structures using angle-resolved photoemission spectroscopy (ARPES). Here, we demonstrate the synthesis of macroscopic AG by self-assembly of C$_{60}$ molecules on metal surfaces. Our theoretical calculations and ARPES measurements directly confirm the existence of Dirac cones at the $K$ ($K^\prime$) points of the Brillouin zone (BZ), in analogy to natural graphene. These results will stimulate ongoing efforts to explore the exotic properties in artificial lattices and provide an important step forward in the realization of novel molecular quantum devices.

cond-mat.mtrl-sci↗

arXiv2020

Superconductivity and Fermi Surface Nesting in the Candidate Dirac Semimetal NbC

We report the synthesis of single-crystal NbC, a transition metal carbide with various unusual properties. Transport, magnetic susceptibility, and specific heat measurements demonstrate that NbC is a conventional superconductor with a superconducting transition temperature ($T_c$) of 11.5 K. Our theoretical calculations show that NbC is a type-II Dirac semimetal with strong Fermi surface nesting, which is supported by our ARPES measurement results. We also observed the superconducting gaps of NbC using angle-resolved photoemission spectroscopy (ARPES) and found some unconventional behaviors. These intriguing superconducting and topological properties, combined with the high corrosion resistance, make NbC an ideal platform for both fundamental research and device applications.

cond-mat.supr-con↗

arXiv2020

Topological electronic structure in the antiferromagnet HoSbTe

Magnetic topological materials, in which the time-reversal symmetry is broken, host various exotic quantum phenomena, including the quantum anomalous Hall effect, axion insulator states, and Majorana fermions. The study of magnetic topological materials is at the forefront of condensed matter physics. Recently, a variety of magnetic topological materials have been reported, such as Mn$_3$Sn, Co$_3$Sn$_2$S$_2$, Fe$_3$Sn$_2$, and MnBi$_2$Te$_4$. Here, we report the observation of a topological electronic structure in an antiferromagnet, HoSbTe, a member of the ZrSiS family of materials, by angle-resolved photoemission spectroscopy measurements and first-principles calculations. We demonstrate that HoSbTe is a Dirac nodal line semimetal when spin-orbit coupling (SOC) is neglected. However, our theoretical calculations show that the strong SOC in HoSbTe fully gaps out the nodal lines and drives the system to a weak topological insulator state, with each layer being a two-dimensional topological insulator. Because of the strong SOC in HoSbTe, the gap is as large as hundreds of meV along specific directions, which is directly observed by our ARPES measurements. The existence of magnetic order and topological properties in HoSbTe makes it a promising material for realization of exotic quantum devices.

cond-mat.mtrl-sci↗

arXiv2020

Experimental evidence of monolayer AlB$_2$ with symmetry-protected Dirac cones

Monolayer AlB$_2$ is composed of two atomic layers: honeycomb borophene and triangular aluminum. In contrast with the bulk phase, monolayer AlB$_2$ is predicted to be a superconductor with a high critical temperature. Here, we demonstrate that monolayer AlB$_2$ can be synthesized on Al(111) via molecular beam epitaxy. Our theoretical calculations revealed that the monolayer AlB$_2$ hosts several Dirac cones along the $Γ$--M and $Γ$--K directions; these Dirac cones are protected by crystal symmetries and are thus resistant to external perturbations. The extraordinary electronic structure of the monolayer AlB$_2$ was confirmed via angle-resolved photoemission spectroscopy measurements. These results are likely to stimulate further research interest to explore the exotic properties arising from the interplay of Dirac fermions and superconductivity in two-dimensional materials.

cond-mat.mtrl-sci↗