Y. Q. Guo

Bio: Y. Q. Guo is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Magnetic susceptibility & Electrical resistivity and conductivity. The author has an hindex of 6, co-authored 8 publications receiving 102 citations.

Structure, charge ordering and physical properties of LuFe 2 O 4

Abstract: Microstructural properties, phase transitions, and charge ordering of $\mathrm{Lu}{\mathrm{Fe}}_{2}{\mathrm{O}}_{4}$ have been extensively investigated by means of transmission electron microscopy (TEM) in a large temperature range from $20\phantom{\rule{0.3em}{0ex}}\text{to}\phantom{\rule{0.3em}{0ex}}550\phantom{\rule{0.3em}{0ex}}\mathrm{K}$. The experimental results demonstrate that the $\mathrm{Lu}{\mathrm{Fe}}_{2}{\mathrm{O}}_{4}$ crystal is commonly modulated by charge ordering (CO), which is often recognizable by superstructure reflections. The (001) twinning domains as a common defect often appear in the $\mathrm{Lu}{\mathrm{Fe}}_{2}{\mathrm{O}}_{4}$ crystals along the $c$-axis direction, with the crystals across each (001) boundary rotated by 180\ifmmode^\circ\else\textdegree\fi{} with respect to one another. The in situ cooling TEM observations from $300\phantom{\rule{0.3em}{0ex}}\mathrm{K}$ down to $20\phantom{\rule{0.3em}{0ex}}\mathrm{K}$ reveal remarkable alternations of the superstructures, suggesting a complex CO process in the present system. Careful analysis shows that the CO in the frustrated ground state is characterized by a modulation with a wave vector of ${\mathbf{q}}_{1}=(1∕3\phantom{\rule{0.3em}{0ex}}1∕3\phantom{\rule{0.3em}{0ex}}2)$. In situ heating TEM observations from $300\phantom{\rule{0.3em}{0ex}}\text{to}\phantom{\rule{0.3em}{0ex}}550\phantom{\rule{0.3em}{0ex}}\mathrm{K}$ clearly reveal that the CO modulation in $\mathrm{Lu}{\mathrm{Fe}}_{2}{\mathrm{O}}_{4}$ becomes invisible above a critical temperature of about ${T}_{C}=530\phantom{\rule{0.3em}{0ex}}\mathrm{K}$. These facts suggest that the CO should be the essential driving force for the structural transitions and ferroelectricity observed in this kind of layered material. Experimental measurements on the ferroelectricity show that the $\mathrm{Lu}{\mathrm{Fe}}_{2}{\mathrm{O}}_{4}$ material, in general, has a large dielectric constant of about 10 000 at room temperature. In order to understand the properties of low-temperature phase transitions, the magnetization and specific heat from $300\phantom{\rule{0.3em}{0ex}}\text{to}\phantom{\rule{0.3em}{0ex}}4\phantom{\rule{0.3em}{0ex}}\mathrm{K}$ have been briefly discussed.

Automatic differentiation for second renormalization of tensor networks

Abstract: Tensor renormalization group (TRG) constitutes an important methodology for accurate simulations of strongly correlated lattice models. Facilitated by the automatic differentiation technique widely used in deep learning, we propose a uniform framework of differentiable TRG ($\ensuremath{\partial}\mathrm{TRG}$) that can be applied to improve various TRG methods, in an automatic fashion. $\ensuremath{\partial}\mathrm{TRG}$ systematically extends the essential concept of second renormalization [Phys. Rev. Lett. 103, 160601 (2009)] where the tensor environment is computed recursively in the backward iteration. Given the forward TRG process, $\ensuremath{\partial}\mathrm{TRG}$ automatically finds the gradient of local tensors through backpropagation, with which one can deeply ``train'' the tensor networks. We benchmark $\ensuremath{\partial}\mathrm{TRG}$ in solving the square-lattice Ising model, and we demonstrate its power by simulating one- and two-dimensional quantum systems at finite temperature. The global optimization as well as GPU acceleration renders $\ensuremath{\partial}\mathrm{TRG}$ a highly efficient and accurate many-body computation approach.

Low-temperature magnetic and transport properties of layered SrxCoO2

Abstract: A series of polycrystalline samples of SrxCoO2 (015 <= x <= 040) have been prepared by a low-temperature ion exchange technique These SrxCoO2 samples are isomorphic to NaxCoO2 with the hexagonal structure, but behave differently from NaxCoO2 with the change of cation concentration For all the samples, the magnetic susceptibility decreases with increasing temperature and shows a Curie-Weiss behavior at high temperatures The low-temperature magnetic susceptibility can be changed considerably by an applied field The specific-heat coefficient gamma=C/T of SrxCoO2 as a function of T-2 turns downwards at low temperatures This is different from the upturn behavior of low-temperature gamma in NaxCoO2 The resistivity of SrxCoO2 changes significantly with the strontium content x An insulating to metallic crossover or transition is observed with increasing x

In-plane substitution effect on the magnetic properties of the two-dimensional spin-gap system SrCu2(BO3)(2)

Abstract: A series of in-plane substituted compounds, including Cu-site [SrZnxCu2-x(BO3)(2)] and B-site [SrCu2(SixB1-xO3)(2)] substitution, were synthesized by solid state reaction. X-ray diffraction measurements reveal that these compounds are single-phase materials and their in-plane lattice parameter depends systematically on the substituting content x. The magnetic susceptibility in different magnetic fields, the magnetization at different temperatures, and the resistivity at room temperature were measured. It is found that the spin gap deduced from the magnetic susceptibility measurements decreases with increasing of substitution content x in both Cu- and B-site substitution. No superconductivity was found in these substituted compounds.

Multiferroicity: the coupling between magnetic and polarization orders

Abstract: Multiferroics, defined for those multifunctional materials in which two or more kinds of fundamental ferroicities coexist, have become one of the hottest topics of condensed matter physics and materials science in recent years. The coexistence of several order parameters in multiferroics brings out novel physical phenomena and offers possibilities for new device functions. The revival of research activities on multiferroics is evidenced by some novel discoveries and concepts, both experimentally and theoretically. In this review, we outline some of the progressive milestones in this stimulating field, especially for those single-phase multiferroics where magnetism and ferroelectricity coexist. First, we highlight the physical concepts of multiferroicity and the current challenges to integrate the magnetism and ferroelectricity into a single-phase system. Subsequently, we summarize various strategies used to combine the two types of order. Special attention is paid to three novel mechanisms for multiferroicity generation: (1) the ferroelectricity induced by the spin orders such as spiral and E-phase antiferromagnetic spin orders, which break the spatial inversion symmetry; (2) the ferroelectricity originating from the charge-ordered states; and (3) the ferrotoroidic system. Then, we address the elementary excitations such as electromagnons, and the application potentials of multiferroics. Finally, open questions and future research opportunities are proposed.

Tensor-entanglement-filtering renormalization approach and symmetry-protected topological order

Abstract: We study the renormalization group flow of the Lagrangian for statistical and quantum systems by representing their path integral in terms of a tensor network. Using a tensor-entanglement-filtering renormalization approach that removes local entanglement and produces a coarse-grained lattice, we show that the resulting renormalization flow of the tensors in the tensor network has a nice fixed-point structure. The isolated fixedpoint tensors Tinv plus the symmetry group Gsym of the tensors i.e., the symmetry group of the Lagrangian characterize various phases of the system. Such a characterization can describe both the symmetry breaking phases and topological phases, as illustrated by two-dimensional 2D statistical Ising model, 2D statistical loop-gas model, and 1+1D quantum spin-1/2 and spin-1 models. In particular, using such a Gsym,Tinv characterization, we show that the Haldane phase for a spin-1 chain is a phase protected by the time-reversal, parity, and translation symmetries. Thus the Haldane phase is a symmetry-protected topological phase. The Gsym,Tinv characterization is more general than the characterizations based on the boundary spins and string order parameters. The tensor renormalization approach also allows us to study continuous phase transitions between symmetry breaking phases and/or topological phases. The scaling dimensions and the central charges for the critical points that describe those continuous phase transitions can be calculated from the fixed-point tensors at those critical points.

Accurate Determination of Tensor Network State of Quantum Lattice Models in Two Dimensions

Abstract: We have proposed a novel numerical method to calculate accurately physical quantities of the ground state using the tensor network wave function in two dimensions. The tensor network wave function is determined by an iterative projection approach which uses the Trotter-Suzuki decomposition formula of quantum operators and the singular value decomposition of matrix. The norm of the wave function and the expectation value of a physical observable are evaluated by a coarse-grain tensor renormalization group approach. Our method allows a tensor network wave function with a high bond degree of freedom (such as D=8) to be handled accurately and efficiently in the thermodynamic limit. For the Heisenberg model on a honeycomb lattice, our results for the ground state energy and the staggered magnetization agree well with those obtained by the quantum Monte Carlo and other approaches.

Learning phase transitions by confusion

Abstract: A neural-network technique can exploit the power of machine learning to mine the exponentially large data sets characterizing the state space of condensed-matter systems. Topological transitions and many-body localization are first on the list. Classifying phases of matter is key to our understanding of many problems in physics. For quantum-mechanical systems in particular, the task can be daunting due to the exponentially large Hilbert space. With modern computing power and access to ever-larger data sets, classification problems are now routinely solved using machine-learning techniques1. Here, we propose a neural-network approach to finding phase transitions, based on the performance of a neural network after it is trained with data that are deliberately labelled incorrectly. We demonstrate the success of this method on the topological phase transition in the Kitaev chain2, the thermal phase transition in the classical Ising model3, and the many-body-localization transition in a disordered quantum spin chain4. Our method does not depend on order parameters, knowledge of the topological content of the phases, or any other specifics of the transition at hand. It therefore paves the way to the development of a generic tool for identifying unexplored phase transitions.

Formation of Pancakelike Ising Domains and Giant Magnetic Coercivity in Ferrimagnetic LuFe2O4

Abstract: We have studied quasi-two-dimensional multiferroic ${\mathrm{LuFe}}_{2}{\mathrm{O}}_{4}$ with strong charge-spin-lattice coupling, in which low-temperature coercivity approaches an extraordinary value of 9 T in single crystals. The enhancement of the coercivity is connected to the collective freezing of nanoscale pancakelike ferrimagnetic domains with large uniaxial magnetic anisotropy (``Ising pancakes''). Our results suggest that collective freezing in low-dimensional magnets with large uniaxial anisotropy provides an effective mechanism to achieve enhanced coercivity. This observation may help identify novel approaches for synthesis of magnets with enhanced properties.