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Teng Yang

Publications and source records attributed to Teng Yang.

At least 19 recordsLinked to original sources

A substrate booster for P-type 2D ferromagnetic semiconductor

Spin transistors with its both charge and spin properties tuned via electrostatic gating are believed capable for widespread use, which however have proven challenging due to the extreme rareness of their physical base -- magnetic semiconductors. The latter are limited within very few systems including diluted magnetic semiconductors (DMS) and two-dimensional ferromagnetic semiconductors (2D-FMS), and known to suffer from inadequate gate-tunability of their electric and/or magnetic properties. Here, we show a substrate engineering paradigm by interfacing few-layered Cr$_{2}$Ge$_{2}$Te$_{6}$ (FL-CGT) with an antiferromagnetic insulator CrOCl. Owing to the subtle interfacial charge transfer couplings, CGT can be drastically turned from an ambipolar semiconductor into a high performance P-type semiconductor. When cooled below the Curie temperature, the ON-OFF ratio in such substrate-boosted FMS field-effect transistor (FET) reaches 10$^{5}$ with its coercive field $H_{c}$ of magnetic hysteresis loop tunable by a factor of more than 200$\%$, enabling {gate-assisted magnetic switching in the prototype semiconducting spin transistor architecture}. A crossover from critical power-law scaling to a dual power-law behaviour under heavy hole doping was further observed. Our findings {signify} an efficient interfacial charge transfer and electrically modulated magnetic anisotropy energy supported by calculations. This high performance P-type FMS-FET system suggests that active substrate-boosting paradigm might be a powerful path for the investigation of future gate-tunable spintronic devices.

cond-mat.mes-hall

Layer-parity-defined surface polarization in Nb$_3$Cl$_8$ for excitonic modulation at van der Waals interfaces

The intrinsic symmetry breaking in the breathing kagome lattice of layered Nb$_3$Cl$_8$ provides a unique mechanism for realizing electrically polar surfaces. In each monolayer, the trimerization of Nb atoms breaks inversion and mirror symmetries, generating an out-of-plane electric dipole. The AB-stacked $\alpha$ phase arranges adjacent layer dipoles antiferroelectrically, leaving the uncompensated surface polarization strictly governed by layer parity. Here, using atomic force microscopy operated in Kelvin probe force microscopy mode, we directly visualize layer-dependent polarization states in exfoliated Nb$_3$Cl$_8$ flakes and resolve a pronounced odd-even oscillation of the surface electrostatic potential. Beyond this parity-locked antiferroelectric order, we further identify intralayer polar domains in which local atomic reconstructions of the breathing kagome network reverse the out-of-plane dipole of the surface layer, producing ferroelectric-like stacking configurations. By interfacing monolayer MoSe$_2$ with Nb$_3$Cl$_8$, we demonstrate that these surface-polarization textures effectively modulate adjacent excitonic emission through domain-dependent interfacial band alignment and charge transfer. Our findings establish Nb$_3$Cl$_8$ as an intrinsic layer-polarized van der Waals platform and show that layer parity provides powerful structural degree of freedom for programming excitonic and optoelectronic responses at van der Waals interfaces.

cond-mat.mtrl-sci

2D ferroelectric narrow-bandgap semiconductor Wurtzite' type alpha-In2Se3 and its silicon-compatible growth

2D van der Waals ferroelectrics, particularly alpha-In2Se3, have emerged as an attractive building block for next-generation information storage technologies due to their moderate band gap and robust ferroelectricity stabilized by dipole locking. alpha-In2Se3 can adopt either the distorted zincblende or wurtzite structures; however, the wurtzite phase has yet to be experimental-ly validated, and its large-scale synthesis poses significant challenges. Here, we report an in-situ transport growth of centimeter-scale wurtzite type alpha-In2Se3 films directly on SiO2 substrates using a process combining pulsed laser deposition and chemical vapor deposition. We demonstrate that it is a narrow bandgap ferroelectric semiconductor, featuring a Curie tem-perature exceeding 620 K, a tunable bandgap (0.8-1.6 eV) modulated by charged domain walls, and a large optical absorption coefficient of 1.3 times 10 powers 6 per centemeter. Moreover, light absorption promotes the dynamic conductance range, linearity, and symmetry of the synapse devices, leading to a high recognition accuracy of 92.3 percent in a supervised pattern classification task for neuromorphic computing. Our findings demonstrate a ferroelectric polymorphism of In2Se3, highlighting its potential in ferroelectric synapses for neuromorphic computing.

cond-mat.mtrl-sci

Topological end state and enhanced thermoelectric performance of a supramolecular device

Supramolecular device (SMD) with topological end states and a noncovalent junction is rarely investigated but deemed promising for thermoelectric (TE) applications. We designed a new kind of SMD based on the Su-Schrieffer-Heeger (SSH) chains, and calculated TE properties of it using the non-equilibrium Green's function (NEGF) method. By scaling TE performance under different optimization conditions, we found the best scenario. Our result shows that the existing topological end states indeed give rise to a large value of power factor, rendering a dimensionless figure-of-merit ZT above 2 in a broad range of chemical potential (doping). Moreover, by imposing the system to various perturbations including end state shift, structural change and disorder, we found that the SMD system possesses a prominent switch effect, further optimizing its performance for TE applications.

physics.comp-ph

QR$^2$-code: An open-source program for double resonance Raman spectra

We present an open-source program, QR$^2$-code, that computes double-resonance Raman (DRR) spectra using first-principles calculations. QR$^2$-code can calculate not only two-phonon DRR spectra but also single-resonance Raman spectra and defect-induced DRR spectra. For defect-induced DDR spectra, we simply assume that the electron-defect matrix element of elastic scattering is a constant. Hands-on tutorials for graphene are given to show how to run QR$^2$-code for single-resonance, double-resonance, and defect-induced Raman spectra. We also compare the single-resonance Raman spectra by QR$^2$-code with that by QERaman code. In QR$^2$-code, the energy dispersions of electron and phonon are taken from Quantum ESPRESSO (QE) code, and the electron-phonon matrix element is obtained from the electron-phonon Wannier (EPW) code. All codes, examples, and scripts are available on the GitHub repository.

cond-mat.mtrl-sci

A Spin-dependent Machine Learning Framework for Transition Metal Oxide Battery Cathode Materials

Owing to the trade-off between the accuracy and efficiency, machine-learning-potentials (MLPs) have been widely applied in the battery materials science, enabling atomic-level dynamics description for various critical processes. However, the challenge arises when dealing with complex transition metal (TM) oxide cathode materials, as multiple possibilities of d-orbital electrons localization often lead to convergence to different spin states (or equivalently local minimums with respect to the spin configurations) after ab initio self-consistent-field calculations, which causes a significant obstacle for training MLPs of cathode materials. In this work, we introduce a solution by incorporating an additional feature - atomic spins - into the descriptor, based on the pristine deep potential (DP) model, to address the above issue by distinguishing different spin states of TM ions. We demonstrate that our proposed scheme provides accurate descriptions for the potential energies of a variety of representative cathode materials, including the traditional Li$_x$TMO$_2$ (TM=Ni, Co, Mn, $x$=0.5 and 1.0), Li-Ni anti-sites in Li$_x$NiO$_2$ ($x$=0.5 and 1.0), cobalt-free high-nickel Li$_x$Ni$_{1.5}$Mn$_{0.5}$O$_4$ ($x$=1.5 and 0.5), and even a ternary cathode material Li$_x$Ni$_{1/3}$Co$_{1/3}$Mn$_{1/3}$O$_2$ ($x$=1.0 and 0.67). We highlight that our approach allows the utilization of all ab initio results as a training dataset, regardless of the system being in a spin ground state or not. Overall, our proposed approach paves the way for efficiently training MLPs for complex TM oxide cathode materials.

cond-mat.mtrl-sci

QERaman: An open-source program for calculating resonance Raman spectra based on Quantum ESPRESSO

We present an open-source program QERaman that computes first-order resonance Raman spectroscopy of materials using the output data from Quantum ESPRESSO. Complex values of Raman tensors are calculated based on the quantum description of the Raman scattering from calculations of electron-photon and electron-phonon matrix elements, which are obtained by using the modified Quantum ESPRESSO. Our program also calculates the resonant Raman spectra as a function of incident laser energy for linearly- or circularly-polarized light. Hands-on tutorials for graphene and MoS$_2$ are given to show how to run QERaman. All codes, examples, and scripts are available on the GitHub repository.

cond-mat.mtrl-sci

Deep Learning Illuminates Spin and Lattice Interaction in Magnetic Materials

Atomistic simulations hold significant value in clarifying crucial phenomena such as phase transitions and energy transport in materials science. Their success stems from the presence of potential energy functions capable of accurately depicting the relationship between system energy and lattice changes. In magnetic materials, two atomic scale degrees of freedom come into play: the lattice and the spin. However, accurately tracing the simultaneous evolution of both lattice and spin in magnetic materials at an atomic scale is a substantial challenge. This is largely due to the complexity involved in depicting the interaction energy precisely, and its influence on lattice and spin-driving forces, such as atomic force and magnetic torque, which continues to be a daunting task in computational science. Addressing this deficit, we present DeepSPIN, a versatile approach that generates high-precision predictive models of energy, atomic forces, and magnetic torque in magnetic systems. This is achieved by integrating first-principles calculations of magnetic excited states with deep learning techniques via active learning. We thoroughly explore the methodology, accuracy, and scalability of our proposed model in this paper. Our technique adeptly connects first-principles computations and atomic-scale simulations of magnetic materials. This synergy presents opportunities to utilize these calculations in devising and tackling theoretical and practical obstacles concerning magnetic materials.

cond-mat.mtrl-sci

First-principles calculations of double resonance Raman spectra for monolayer MoTe$_2$

Since double resonance Raman (DRR) spectra are laser-energy dependent, the first-principles calculations of DRR for two-dimensional materials are challenging. Here, the DRR spectrum of monolayer MoTe$_2$ is calculated by home-made program, in which we combine {\em ab-initio} density-functional-theory calculations with the electron-phonon Wannier (EPW) method. Within the fourth-order perturbation theory, we are able to quantify not only the electron-photon matrix elements within the dipole approximation, but also the electron-phonon matrix elements using the Wannier functions. The reasonable agreement between the calculated and experimental Raman spectra is achieved, in which we reproduce some distinctive features of transition metal dichalcogenides (TMDCs) from graphene (for example, the dominant intervalley process involving an electron or a hole). Furthermore, we perform an analysis of the possible DRR modes over the Brillouin zone, highlighting the role of low-symmetry points. Raman tensors for some DRR modes are given by first principles calculations from which laser polarization dependence is obtained.

cond-mat.mtrl-sci

Dimer rattling mode induced low thermal conductivity in an excellent acoustic conductor

A solid with larger sound speeds exhibits higher lattice thermal conductivity (k_{lat}). Diamond is a prominent instance where its mean sound speed is 14400 m s-1 and k_{lat} is 2300 W m-1 K-1. Here, we report an extreme exception that CuP2 has quite large mean sound speeds of 4155 m s-1, comparable to GaAs, but the single crystals show a very low lattice thermal conductivity of about 4 W m-1 K-1 at room temperature, one order of magnitude smaller than GaAs. To understand such a puzzling thermal transport behavior, we have thoroughly investigated the atomic structure and lattice dynamics by combining neutron scattering techniques with first-principles simulations. Cu atoms form dimers sandwiched in between the layered P atomic networks and the dimers vibrate as a rattling mode with frequency around 11 meV. This mode is manifested to be remarkably anharmonic and strongly scatters acoustic phonons to achieve the low k_{lat}. Such a dimer rattling behavior in layered structures might offer an unprecedented strategy for suppressing thermal conduction without involving atomic disorder.

cond-mat.mtrl-sci

Room temperature 2D ferromagnetism in few-layered 1$T$-CrTe$_{2}$

Spin-related electronics using two dimensional (2D) van der Waals (vdW) materials as a platform are believed to hold great promise for revolutionizing the next generation spintronics. Although many emerging new phenomena have been unravelled in 2D electronic systems with spin long-range orderings, the scarcely reported room temperature magnetic vdW material has thus far hindered the related applications. Here, we show that intrinsic ferromagnetically aligned spin polarization can hold up to 316 K in a metallic phase of 1$T$-CrTe$_{2}$ in the few-layer limit. This room temperature 2D long range spin interaction may be beneficial from an itinerant enhancement. Spin transport measurements indicate an in-plane room temperature negative anisotropic magnetoresistance (AMR) in few-layered CrTe$_{2}$, but a sign change in the AMR at lower temperature, with -0.6$\%$ at 300 K and +5$\%$ at 10 K, respectively. This behavior may originate from the specific spin polarized band structure of CrTe$_{2}$. Our findings provide insights into magnetism in few-layered CrTe$_{2}$, suggesting potential for future room temperature spintronic applications of such 2D vdW magnets.

cond-mat.mes-hall

Gate tunable giant anisotropic resistance in ultra-thin GaTe

In crystals, the duplication of atoms often follows different periodicity along different directions. It thus gives rise to the so called anisotropy, which is usually even more pronounced in two dimensional (2D) materials due to the absence of $\textbf{z}$ dimension. Indeed, in the emerging 2D materials, electrical anisotropy has been one of the focuses in recent experimental efforts. However, key understandings of the in-plane anisotropic resistance in low-symmetry 2D materials, as well as demonstrations of model devices taking advantage of it, have proven difficult. Here, we show that, in few-layered semiconducting GaTe, electrical conductivity along $\textbf{x}$ and $\textbf{y}$ directions of the 2D crystal can be gate tuned from a ratio of less than one order to as large as 10$^{3}$. This effect is further demonstrated to yield an anisotropic memory resistor behaviour in ultra-thin GaTe, when equipped with an architecture of van der Waals floating gate. Our findings of gate tunable giant anisotropic resistance (GAR) effect pave the way for potential applications in nano-electronics such as multifunctional directional memories in the 2D limit.

cond-mat.mes-hall

New two-dimensional phase of tin chalcogenides: candidates for high-performance thermoelectric materials

Tin-chalcogenides SnX (X = Te, Se and S) have been arousing research interest due to their thermoelectric physical properties. The two-dimensional (2D) counterparts, which are expected to enhance the property, nevertheless, have not been fully explored because of many possible structures. Generating variable composition of 2D Sn$_{1-x}$X$_{x}$ systems (X = Te, Se and S) has been performed using global searching method based on evolutionary algorithm combining with density functional calculations. A new hexagonal phase named by $\beta'$-SnX is found by Universal Structure Predictor Evolutionary Xtallography (USPEX), and the structural stability has been further checked by phonon dispersion calculation and the elasticity criteria. The $\beta'$-SnTe is the most stable among all possible 2D phases of SnTe including those experimentally available phases. Further, $\beta'$ phases of SnSe and SnS are also found energetically close to the most stable phases. High thermoelectronic (TE) performance has been achieved in the $\beta'$-SnX phases, which have dimensionless figure of merit (ZT) as high as $\sim$0.96 to 3.81 for SnTe, $\sim$0.93 to 2.51 for SnSe and $\sim$1.19 to 3.18 for SnS at temperature ranging from 300 K to 900 K with practically attainable carrier concentration of 5$\times$10$^{12}$ cm$^{-2}$. The high TE performance is resulted from a high power factor which is attributed to the quantum confinement of 2D materials and the band convergence near Fermi level, as well as low thermal conductivity mainly from both low elastic constants due to weak inter-Sn bonding strength and strong lattice anharmonicity.

cond-mat.mtrl-sci

Electric-field control of magnetism in few-layered van der Waals magnet

Manipulating quantum state via electrostatic gating has been intriguing for many model systems in nanoelectronics. When it comes to the question of controlling the electron spins, more specifically, the magnetism of a system, tuning with electric field has been proven to be elusive. Recently, magnetic layered semiconductors have attracted much attention due to their emerging new physical phenomena. However, challenges still remain in the demonstration of a gate controllable magnetism based on them. Here, we show that, via ionic gating, strong field effect can be observed in few-layered semiconducting Cr$_{2}$Ge$_{2}$Te$_{6}$ devices. At different gate doping, micro-area Kerr measurements in the studied devices demonstrate tunable magnetization loops below the Curie temperature, which is tentatively attributed to the moment re-balance in the spin-polarized band structure. Our findings of electric-field controlled magnetism in van der Waals magnets pave the way for potential applications in new generation magnetic memory storage, sensors, and spintronics.

cond-mat.mes-hall

Spontaneous antiferromagnetic order and strain effect on electronic properties of ${\alpha}$-graphyne

Using hybrid exchange-correlation functional in ab initio density functional theory calculations, we study magnetic properties and strain effect on the electronic properties of $\alpha$-graphyne monolayer. We find that a spontaneous antiferromagnetic (AF) ordering occurs with energy band gap ($\sim$ 0.5 eV) in the equilibrated $\alpha$-graphyne. Bi-axial tensile strain enhances the stability of AF state as well as the staggered spin moment and value of the energy gap. The antiferromagnetic semiconductor phase is quite robust against moderate carrier filling with threshold carrier density up to 1.7$\times$10$^{14}$ electrons/cm$^2$ to destabilize the phase. The spontaneous AF ordering and strain effect in $\alpha$-graphyne can be well described by the framework of the Hubbard model. Our study shows that it is essential to consider the electronic correlation effect properly in $\alpha$-graphyne and may pave an avenue for exploring magnetic ordering in other carbon allotropes with mixed hybridization of s and p orbitals.

physics.comp-ph

Fundamental Band Gap and Alignment of Two-Dimensional Semiconductors Explored by Machine Learning

Two-dimensional (2D) semiconductors isoelectronic to phosphorene has been drawing much attention recently due to their promising applications for next-generation (opt)electronics. This family of 2D materials contains more than 400 members, including (a) elemental group-V materials, (b) binary III-VII and IV-VI compounds, (c) ternary III-VI-VII and IV-V-VII compounds, making materials design with targeted functionality unprecedentedly rich and extremely challenging. To shed light on rational functionality design with this family of materials, we systemically explore their fundamental band gaps and alignments using hybrid density functional theory (DFT) in combination with machine learning. First, GGA-PBE and HSE calculations are performed as a reference. We find this family of materials share similar crystalline structures, but possess largely distributed band-gap values ranging approximately from 0 to 8 eV. Then, we apply machine learning methods, including Linear Regression (LR), Random Forest Regression (RFR), and Support Vector Machine Regression (SVR), to build models for prediction of electronic properties. Among these models, SVR is found to have the best performance, yielding the root mean square error (RMSE) less than 0.15 eV for predicted band gaps, VBMs, and CBMs when both PBE results and elemental information are used as features. Thus, we demonstrate machine learning models are universally suitable for screening 2D isoelectronic systems with targeted functionality and especially valuable for the design of alloys and heterogeneous systems.

cond-mat.mes-hall

Double resonance Raman modes in mono- and few-layer MoTe$_2$

We study the second-order Raman process of mono- and few-layer MoTe$_2$, by combining {\em ab initio} density functional perturbation calculations with experimental Raman spectroscopy using 532, 633 and 785 nm excitation lasers. The calculated electronic band structure and the density of states show that the electron-photon resonance process occurs at the high-symmetry M point in the Brillouin zone, where a strong optical absorption occurs by a logarithmic Van-Hove singularity. Double resonance Raman scattering with inter-valley electron-phonon coupling connects two of the three inequivalent M points in the Brillouin zone, giving rise to second-order Raman peaks due to the M point phonons. The predicted frequencies of the second-order Raman peaks agree with the observed peak positions that cannot be assigned in terms of a first-order process. Our study attempts to supply a basic understanding of the second-order Raman process occurring in transition metal di-chalcogenides (TMDs) and may provide additional information both on the lattice dynamics and optical processes especially for TMDs with small energy band gaps such as MoTe$_2$ or at high laser excitation energy.

cond-mat.mtrl-sci

A Deep Graph Embedding Network Model for Face Recognition

In this paper, we propose a new deep learning network "GENet", it combines the multi-layer network architec- ture and graph embedding framework. Firstly, we use simplest unsupervised learning PCA/LDA as first layer to generate the low- level feature. Secondly, many cascaded dimensionality reduction layers based on graph embedding framework are applied to GENet. Finally, a linear SVM classifier is used to classify dimension-reduced features. The experiments indicate that higher classification accuracy can be obtained by this algorithm on the CMU-PIE, ORL, Extended Yale B dataset.

cs.CV