arXiv subjects

Gang Su

Publications and source records attributed to Gang Su.

At least 109 records · Page 6Linked to original sources

arXiv2020

Kagome quantum anomalous Hall effect with high Chern number and large band gap

Due to the potential applications in the low-power-consumption spintronic devices, the quantum anomalous Hall effect (QAHE) has attracted tremendous attention in past decades. However, up to now, QAHE was only observed experimentally in topological insulators with Chern numbers C= 1 and 2 at very low temperatures. Here, we propose three novel two-dimensional stable kagome ferromagnets Co3Pb3S2, Co3Pb3Se2and Co3Sn3Se2that can realize QAHE with high Chern number of |C|=3. Monolayers Co3Pb3S2, Co3Pb3Se2 and Co3Sn3Se2 possess the large band gap of 70, 77 and 63 meV with Curie temperature TC of 51, 42 and 46 K, respectively. By constructing a heterostructure Co3Sn3Se2/MoS2, its TC is enhanced to 60 K and the band gap keeps about 60 meV due to the tensile strain of 2% at the interface. For the bilayer compound Co6Sn5Se4, it becomes a half-metal, with a relatively flat plateau in its anomalous Hall conductivity corresponding to |C| = 3 near the Fermi level. Our results provide new topological nontrivial systems of kagome ferromagnetic monolayers and heterostructrues possessing QAHE with high Chern number |C| = 3 and large band gaps.

cond-mat.mtrl-sci↗

arXiv2020

Voting Data-Driven Regression Learning for Discovery of Functional Materials and Applications to Two-Dimensional Ferroelectric Materials

Regression machine learning is widely applied to predict various materials. However, insufficient materials data usually leads to a poor performance. Here, we develop a new voting data-driven method that could generally improve the performance of regression learning model for accurately predicting properties of materials. We apply it to investigate a large family (2135) of two-dimensional hexagonal binary compounds focusing on ferroelectric properties and find that the performance of the model for electric polarization is indeed greatly improved, where 38 stable ferroelectrics with out-of-plane polarization including 31 metals and 7 semiconductors are screened out. By an unsupervised learning, actionable information such as how the number and orbital radius of valence electrons, ionic polarizability, and electronegativity of constituent atoms affect polarization was extracted. Our voting data-driven method not only reduces the size of materials data for constructing a reliable learning model but also enables to make precise predictions for targeted functional materials.

cond-mat.mtrl-sci↗

arXiv2020

Large Family of Two-Dimensional Ferroelectric Metals Discovered via Machine Learning

Ferroelectricity and metallicity are usually believed not to coexist because conducting electrons would screen out static internal electric fields. In 1965, Anderson and Blount proposed the concept of 'ferroelectric metal', however, it is only until recently that very rare ferroelectric metals were reported. Here, by combining high-throughput ab initio calculations and data-driven machine learning method with new electronic orbital based descriptors, we systematically investigated a large family (2,964) of two-dimensional (2D) bimetal phosphates, and discovered 60 stable ferroelectrics with out-of-plane polarization, including 16 ferroelectric metals and 44 ferroelectric semiconductors that contain seven multiferroics. The ferroelectricity origins from spontaneous symmetry breaking induced by the opposite displacements of bimetal atoms, and the full-d-orbital coinage metal elements cause larger displacements and polarization than other elements. For 2D ferroelectric metals, the odd electrons per unit cell without spin polarization may lead to a half-filled energy band around Fermi level and is responsible for the metallicity. It is revealed that the conducting electrons mainly move on a single-side surface of the 2D layer, while both the ionic and electric contributions to polarization come from the other side and are vertical to the above layer, thereby causing the coexistence of metallicity and ferroelectricity. Van der Waals heterostructures based on ferroelectric metals may enable the change of Schottky barrier height or the Schottky-Ohmic contact type and induce a dramatic change of their vertical transport properties. Our work greatly expands the family of 2D ferroelectric metals and will spur further exploration of 2D ferroelectric metals.

cond-mat.mtrl-sci↗

arXiv2020

Tinselenidene: a Two-dimensional Auxetic Material with Ultralow Lattice Thermal Conductivity and Ultrahigh Hole Mobility

By means of extensive ab initio calculations, a new two-dimensional (2D) atomic material tin selenide monolayer (coined as tinselenidene) is predicted to be a semiconductor with an indirect gap (1.45 eV) and a high hole mobility (of order 10000 cm2V-1S-1), and will bear an indirect-direct gap transition under a rather low strain (<0.5 GPa). Tinselenidene has a very small Young's modulus (20-40 GPa) and an ultralow lattice thermal conductivity (<3 Wm-1K-1 at 300 K), making it probably the most flexible and most heat-insulating material in known 2D atomic materials. In addition, tinseleniden has a large negative Poisson's ratio of -0.17, thus could act as a 2D auxetic material. With these intriguing properties, tinselenidene could have wide potential applications in thermoelectrics, nanomechanics and optoelectronics.

cond-mat.mtrl-sci↗

arXiv2020

Visual configuration segmentation of quantum states for phase identification in many-body systems

Artificial intelligence provides an unprecedented perspective for studying phases of matter in condensed-matter systems. Image segmentation is a basic technique of computer vision that belongs to a branch of artificial intelligence. In this work, we propose a segmentation scheme named visual configuration segmentation (VCS) to unveil quantum phases and quantum phase transitions in many-body systems. By encoding the information of renormalized quantum states into a color image and segmenting the color image through the VCS, the renormalized quantum states can be visualized, from which quantum phase transitions can be revealed and the corresponding critical points can be identified. Our scheme is benchmarked on several strongly correlated spin systems, which does not depend on the priori knowledge of order parameters of quantum phases. This demonstrates the potential to disclose the underlying structure of quantum phases by the techniques of computer vision.

cond-mat.str-el↗

arXiv2020

Antiferromagnetic and Electric Polarized States in Two-Dimensional Janus Semiconductor Fe2Cl3I3

Two-dimensional (2D) Janus semiconductors with mirror asymmetry can introduce novel properties, such as large spin-orbit coupling (SOC) and normal piezoelectric polarization, which have attracted a great interest for their potential applications. Inspired by the recently fabricated 2D ferromagnetic (FM) semiconductor CrI3, a stable 2D (in x-y plane) antiferromagnetic (AFM) Janus semiconductor Fe2Cl3I3 with normal sublattice magnetization (m//z) is obtained by density functional theory calculations. By applying a tensile strain, the four magnetic states sequentially occur: AFM with m//z of sublattice, AFM with m//xy of sublattice, FM with m//xy, and FM with m//z. Such novel magnetic phase diagram driven by strain can be well understood by the spin-spin interactions including the third nearest-neighbor hoppings with the single-ion anisotropy, in which the SOC of I atoms is found to play an essential role. In addition, the electric polarization of Fe2Cl3I3 preserves with strain due to the broken inversion symmetry. Our results predict the rare Janus material Fe2Cl3I3 as an example of 2D semiconductors with both spin and charge polarizations, and reveal the highly sensitive strain-controlled magnetic states and magnetization direction, which highlights the 2D magnetic Janus semiconductor as a new platform to design spintronic materials.

cond-mat.mtrl-sci↗

arXiv2020

Great enhancement of Curie temperature and magnetic anisotropy in two-dimensional van der Waals magnetic semiconductor heterostructures

In two-dimensional (2D) magnetic systems, large magnetic anisotropy is needed to stabilize the magnetic order according to Mermin-Wagner theorem. Based on density functional theory (DFT) calculations, we propose that the magnetic anisotropic energy (MAE) of 2D ferromagnetic (FM) semiconductors can be strongly enhanced in van der Waals heterostructures by attaching a nonmagnetic semiconductor monolayer with large spin-orbit coupling. We studied Cr2Ge2Te6/PtSe2 bilayer heterostructures, where each layer has been realized in recent experiments. The DFT calculations show that the MAE of Cr2Ge2Te6/PtSe2 is enhanced by 70%, and the Curie temperature TC is increased far beyond room temperature. A model Hamiltonian is suggested to analyze the DFT results, showing that both the Dzyaloshinskii-Moriya interaction and the single-ion anisotropy contribute to the enhancement of the MAE. Based on the superexchange picture, we find that the decreased energy difference between 3d orbitals of Cr and 5p orbitals of Te contributes partially to the increase of TC. Our present work indicates a promising way to enhance the MAE and TC by constructing van der Waals semiconductor heterostructures, which will inspire further studies on the 2D magnetic semiconductor systems.

cond-mat.mtrl-sci↗

arXiv2020

Visualizing Quantum Phases And Identifying Quantum Phase Transitions By Nonlinear Dimensionality Reduction

Identifying quantum phases and phase transitions is key to understand complex phenomena in statistical physics. In this work, we propose an unconventional strategy to access quantum phases and phase transitions by visualization based on the distribution of ground states in Hilbert space. By mapping the quantum states in Hilbert space onto a two-dimensional feature space using an unsupervised machine learning method, distinct phases can be directly specified and quantum phase transitions can be well identified. Our proposal is benchmarked on gapped, critical, and topological phases in several strongly correlated spin systems. As this proposal directly learns quantum phases and phase transitions from the distributions of the quantum states, it does not require priori knowledge of order parameters of physical systems, which thus indicates a perceptual route to identify quantum phases and phase transitions particularly in complex systems by visualization through learning.

cond-mat.str-el↗

arXiv2020

Toward understanding physical origin of 2175Å extinction bump in interstellar medium

The 2175 Å ultraviolet (UV) extinction bump in interstellar medium (ISM) of the Milky Way was discovered in 1965. After intensive exploration of more than a half century, however, its exact origin still remains a big conundrum that is being debated. Here we propose a mixture model by which the extinction bump in ISM is argued possibly relevant to the clusters of hydrogenated T-carbon (HTC) molecules (C40H16) that have intrinsically a sharp absorption peak at the wavelength 2175 Å. By linearly combining the calculated absorption spectra of HTC mixtures, graphite, MgSiO3 and Fe2SiO4, we show that the UV extinction curves of optional six stars can be nicely fitted. This present work poses an alternative explanation toward understanding the physical origin of the 2175 Å extinction bump in ISM of the Milky Way.

astro-ph.GA↗

arXiv2020

Superconductivity in sodium-doped T-carbon

T-carbon has been proposed as a new carbon allotrope in 2011, which was successfully synthesized in recent experiments. Because of its fluffy structure, several kinds of atoms can be intercalated into T-carbon, making it a versatile candidate in various applications such as hydrogen storage, perovskite solar cells, lithium ion batteries, thermoelectrics, photocatalyst, etc. Here we show that superconductivity can appear in Na-doped T-carbon with superconducting transition temperature Tc of 11 K at ambient pressure, and Tc can be enhanced to 19 K under pressure of 14 GPa, which results from an enhancement of the electron-phonon coupling due to the shift of the phonon spectral weight to lower frequencies with the increase of pressure. The calculations on specific heat and electrical and thermal conductivities show that the normal state of the Na-doped T-carbon superconductor reveals a non-Fermi liquid behavior. The prediction of superconductivity in Na-doped T-carbon would spur great interest both experimentally and theoretically to explore novel carbon-based superconductors.

cond-mat.supr-con↗

arXiv2020

Flat Band and Hole-induced Ferromagnetism in a Novel Carbon Monolayer

In recent experiments, superconductivity and correlated insulating states were observed in twisted bilayer graphene (TBG) with small magic angles, which highlights the importance of the flat bands near Fermi energy. However, the moiré pattern of TBG consists of more than ten thousand carbon atoms that is not easy to handle with conventional methods. By density functional theory calculations, we obtain a flat band at E$_F$ in a novel carbon monolayer coined as cyclicgraphdiyne with the unit cell of eighteen atoms. By doping holes into cyclicgraphdiyne to make the flat band partially occupied, we find that cyclicgraphdiyne with 1/8, 1/4, 3/8 and 1/2 hole doping concentration shows ferromagnetism (half-metal) while the case without doping is nonmagnetic, indicating a hole-induced nonmagnetic-ferromagnetic transition. The calculated conductivity of cyclicgraphdiyne with 1/8, 1/4 and 3/8 hole doping concentration is much higher than that without doping or with 1/2 hole doping. These results make cyclicgraphdiyne really attractive. By studying several carbon monolayers, we find that a perfect flat band may occur in the lattices with both separated or corner-connected triangular motifs with only including nearest-neighboring hopping of electrons, and the dispersion of flat band can be tuned by next-nearest-neighboring hopping. Our results shed insightful light on the formation of flat band in TBG. The present study also poses an alternative way to manipulate magnetism through doping flat band in carbon materials.

cond-mat.mtrl-sci↗

arXiv2020

Tangent-Space Gradient Optimization of Tensor Network for Machine Learning

The gradient-based optimization method for deep machine learning models suffers from gradient vanishing and exploding problems, particularly when the computational graph becomes deep. In this work, we propose the tangent-space gradient optimization (TSGO) for the probabilistic models to keep the gradients from vanishing or exploding. The central idea is to guarantee the orthogonality between the variational parameters and the gradients. The optimization is then implemented by rotating parameter vector towards the direction of gradient. We explain and testify TSGO in tensor network (TN) machine learning, where the TN describes the joint probability distribution as a normalized state $\left| ψ\right\rangle $ in Hilbert space. We show that the gradient can be restricted in the tangent space of $\left\langle ψ\right.\left| ψ\right\rangle = 1$ hyper-sphere. Instead of additional adaptive methods to control the learning rate in deep learning, the learning rate of TSGO is naturally determined by the angle $θ$ as $η= \tan θ$. Our numerical results reveal better convergence of TSGO in comparison to the off-the-shelf Adam.

cs.LG↗

arXiv2020

Two-dimensional magnetic semiconductors with room Curie temperatures

We propose two-dimensional (2D) Ising-type ferromagnetic semiconductors TcSiTe3, TcGeSe3, and TcGeTe3 with high Curie temperatures around 200-0500 K. Owing to large spin-orbit couplings, the large magnetocrystalline anisotropy energy (MAE), large anomalous Hall conductivity, and large magneto-optical Kerr effect were discovered in these intriguing 2D materials. By comparing all possible 2D MGeTe3 materials (M = 3d, 4d, 5d transition metals), we found a large orbital moment around 0.5 $μ$B per atom and a large MAE for TcGeTe3. The large orbital moments are revealed to be from the comparable crystal fields and electron correlations in these Tc-based 2D materials. The microscopic mechanism of the high Curie temperature is also addressed. Our findings reveal the unique magnetic behaviors of 2D Tc-based materials and present a family of 2D ferromagnetic semiconductors with large MAE and Kerr rotation angles that would have wide applications in designing spintronic devices.

cond-mat.mtrl-sci↗

arXiv2019

Unidirectional Seebeck effect in magnetic topological insulators

We theoretically investigate the temperature gradient-dependent or unidirectional Seebeck effect (USE) in a magnetic/ nonmagnetic topological insulator (TI) heterostructure with in-plane magnetization in terms of the semiclassical electron dynamics and Fermi golden rule. The USE has a quantum origin arising from the magnon asymmetric scattering of surface Dirac electrons on TI. We discuss the USE in the heterostructures, Cr$_{x}$(Bi$_{1-y}$Sb$_y$)$_{2-x}$Te$_{3}$/ (Bi$_{1-y}$Sb$_{y})_2$Te$_{3}$. The USE exhibits $\cosϕ$-dependence (measured from $y$-direction) on the orientation of magnetization. It's found that the sign of USE stays unchanged when the system is transferred from $p$-doping to $n$-doping. The USE shows on inverse-linearly temperature dependent at high temperature.

cond-mat.mes-hall↗

arXiv2019

Quantum Compressed Sensing with Unsupervised Tensor-Network Machine Learning

We propose tensor-network compressed sensing (TNCS) by combining the ideas of compressed sensing, tensor network (TN), and machine learning, which permits novel and efficient quantum communications of realistic data. The strategy is to use the unsupervised TN machine learning algorithm to obtain the entangled state $|Ψ\rangle$ that describes the probability distribution of a huge amount of classical information considered to be communicated. To transfer a specific piece of information with $|Ψ\rangle$, our proposal is to encode such information in the separable state with the minimal distance to the measured state $|Φ\rangle$ that is obtained by partially measuring on $|Ψ\rangle$ in a designed way. To this end, a measuring protocol analogous to the compressed sensing with neural-network machine learning is suggested, where the measurements are designed to minimize uncertainty of information from the probability distribution given by $|Φ\rangle$. In this way, those who have $|Φ\rangle$ can reliably access the information by simply measuring on $|Φ\rangle$. We propose q-sparsity to characterize the sparsity of quantum states and the efficiency of the quantum communications by TNCS. The high q-sparsity is essentially due to the fact that the TN states describing nicely the probability distribution obey the area law of entanglement entropy. Testing on realistic datasets (hand-written digits and fashion images), TNCS is shown to possess high efficiency and accuracy, where the security of communications is guaranteed by the fundamental quantum principles.

stat.ML↗

arXiv2019

Accelerated Discovery of Two-Dimensional Optoelectronic Octahedral Oxyhalides via High-Throughput Ab Initio Calculations and Machine Learning

Traditional trial-and-error methods are obstacles for large-scale searching of new optoelectronic materials. Here, we introduce a method combining high-throughput ab initio calculations and machine-learning approaches to predict two-dimensional octahedral oxyhalides with improved optoelectronic properties. We develop an effective machine-learning model based on an expansive dataset generated from density functional calculations including the geometric and electronic properties of 300 two-dimensional octahedral oxyhalides. Our model accelerates the screening of potential optoelectronic materials of 5,000 two-dimensional octahedral oxyhalides. The distorted stacked octahedral factors proposed in our model play essential roles in the machine-learning prediction. Several potential two-dimensional optoelectronic octahedral oxyhalides with moderate band gaps, high electron mobilities, and ultrahigh absorbance coefficients are successfully hypothesized.

cond-mat.mtrl-sci↗

arXiv2019

Two-Dimensional Room Temperature Ferromagnetic Semiconductors with Quantum Anomalous Hall Effect

To obtain room temperature ferromagnetic semiconductors is one of big challenges in science, and also premises essentially to realize room temperature quantum anomalous Hall effect (QAHE), both of which are quite expected for a long time. Here we report that, based on first-principles calculations, PdBr3, PtBr3, PdI3 and PtI3 monolayers are ferromagnetic semiconductors with possible high temperature QAHE. Monte Carlo simulations give Curie temperatures 350 K and 375 K for PdBr3 and PtBr3 monolayers, respectively. These two-dimensional (2D) materials are Chern insulators. The band gaps of PdBr3 and PtBr3 are 58.7 meV and 28.1 meV with GGA and 100.8 meV and 45 meV with HSE06, respectively, quite well in favor of observing room temperature QAHE. The large band gaps were unveiled from multi-orbital electron correlations. By carefully checking the stability, PdBr3, PtBr3, and PtI3 monolayers are all predicted to be feasible in experiment. The present work sheds new light on developing promising spintronic devices by using the room temperature ferromagnetic semiconductors, and dissipationless devices by applying room temperature QAHE.

cond-mat.mtrl-sci↗

arXiv2019

Lecture Notes of Tensor Network Contractions

Tensor network (TN), a young mathematical tool of high vitality and great potential, has been undergoing extremely rapid developments in the last two decades, gaining tremendous success in condensed matter physics, atomic physics, quantum information science, statistical physics, and so on. In this lecture notes, we focus on the contraction algorithms of TN as well as some of the applications to the simulations of quantum many-body systems. Starting from basic concepts and definitions, we first explain the relations between TN and physical problems, including the TN representations of classical partition functions, quantum many-body states (by matrix product state, tree TN, and projected entangled pair state), time evolution simulations, etc. These problems, which are challenging to solve, can be transformed to TN contraction problems. We present then several paradigm algorithms based on the ideas of the numerical renormalization group and/or boundary states, including density matrix renormalization group, time-evolving block decimation, coarse-graining/corner tensor renormalization group, and several distinguished variational algorithms. Finally, we revisit the TN approaches from the perspective of multi-linear algebra (also known as tensor algebra or tensor decompositions) and quantum simulation. Despite the apparent differences in the ideas and strategies of different TN algorithms, we aim at revealing the underlying relations and resemblances in order to present a systematic picture to understand the TN contraction approaches.

physics.comp-ph↗