arXiv subjects

Pengfei Zhou

Publications and source records attributed to Pengfei Zhou.

76 records · Page 5Linked to original sources

arXiv2020

Colossal Negative Magnetoresistance Effect in A La$_{1.37}$Sr$_{1.63}$Mn$_2$O$_7$ Single Crystal Grown by Laser-Diode-Heated Floating-Zone Technique

We have grown La$_{1.37}$Sr$_{1.63}$Mn$_2$O$_7$ single crystals with a laser-diode-heated floating-zone furnace and studied the crystallinity, structure, and magnetoresistance (MR) effect by in-house X-ray Laue diffraction, X-ray powder diffraction, and resistance measurements. The La$_{1.37}$Sr$_{1.63}$Mn$_2$O$_7$ single crystal crystallizes into a tetragonal structure with space group \emph{I}4{/}\emph{mmm} at room temperature. At 0 T, the maximum resistance centers around $\sim$166.9 K. Below $\sim$35.8 K, it displays an insulating character with an increase in resistance upon cooling. An applied magnetic field of \emph{B}~=~7~T strongly suppresses the resistance indicative of a negative MR effect. The minimum MR value equals $-$91.23\% at 7 T and 128.7 K. The magnetic-field-dependent resistance shows distinct features at 1.67, 140, and 322 K, from which we calculated the corresponding MR values. At 14 T and 140 K, the colossal negative MR value is down to $-$94.04(5)\%. We schematically fit the MR values with different models for an ideal describing of the interesting features of the MR value versus \emph{B} curves.

cond-mat.str-el↗

arXiv2020

Super-Necking Crystal Growth and Structural and Magnetic Properties of SrTb$_2$O$_4$ Single Crystals

We report on single-crystal growths of the SrTb$_2$O$_4$ compound by a super-necking technique with a laser-floating-zone furnace and study the stoichiometry, growth mode, and structural and magnetic properties by scanning electronic microscopy, neutron Laue, X-ray powder diffraction, and the physical property measurement system. We optimized the growth parameters, mainly the growth speed, atmosphere, and the addition of a Tb$_4$O$_7$ raw material. Neutron Laue diffraction displays the characteristic feature of a single crystal. Our study reveals an atomic ratio of Sr:Tb $ = 0.97(2){:}2.00(1)$ and a possible layer by layer crystal growth mode. Our X-ray powder diffraction study determines the crystal structure, lattice constants and atomic positions. The paramagnetic (PM) Curie--Weiss (CW) temperature $θ_{\texttt{CW}} =$ 5.00(4) K, and the effective PM moment $M^{\texttt{eff}}_{\texttt{mea}} =$ 10.97(1) $μ_\texttt{B}$ per Tb$^{3+}$ ion. The data of magnetization versus temperature can be divided into three regimes, showing a coexistence of antiferromagnetic and ferromagnetic interactions. This probably leads to the magnetic frustration in the SrTb$_2$O$_4$ compound. The magnetization at 2 K and 14 T originates from both the Tb1 and Tb2 sites and is strongly frustrated with an expected saturation field at $\sim$41.5 T, displaying an intricate phase diagram with three ranges.

cond-mat.mtrl-sci↗

arXiv2020

Solving Statistical Mechanics on Sparse Graphs with Feedback Set Variational Autoregressive Networks

We propose a method for solving statistical mechanics problems defined on sparse graphs. It extracts a small Feedback Vertex Set (FVS) from the sparse graph, converting the sparse system to a much smaller system with many-body and dense interactions with an effective energy on every configuration of the FVS, then learns a variational distribution parameterized using neural networks to approximate the original Boltzmann distribution. The method is able to estimate free energy, compute observables, and generate unbiased samples via direct sampling without auto-correlation. Extensive experiments show that our approach is more accurate than existing approaches for sparse spin glasses. On random graphs and real-world networks, our approach significantly outperforms the standard methods for sparse systems such as the belief-propagation algorithm; on structured sparse systems such as two-dimensional lattices our approach is significantly faster and more accurate than recently proposed variational autoregressive networks using convolution neural networks.

cond-mat.stat-mech↗

arXiv2019

Phase transitions and optimal algorithms for semi-supervised classifications on graphs: from belief propagation to graph convolution network

We perform theoretical and algorithmic studies for the problem of clustering and semi-supervised classification on graphs with both pairwise relational information and single-point feature information, upon a joint stochastic block model for generating synthetic graphs with both edges and node features. Asymptotically exact analysis based on the Bayesian inference of the underlying model are conducted, using the cavity method in statistical physics. Theoretically, we identify a phase transition of the generative model, which puts fundamental limits on the ability of all possible algorithms in the clustering task of the underlying model. Algorithmically, we propose a belief propagation algorithm that is asymptotically optimal on the generative model, and can be further extended to a belief propagation graph convolution neural network (BPGCN) for semi-supervised classification on graphs. For the first time, well-controlled benchmark datasets with asymptotially exact properties and optimal solutions could be produced for the evaluation of graph convolution neural networks, and for the theoretical understanding of their strengths and weaknesses. In particular, on these synthetic benchmark networks we observe that existing graph convolution neural networks are subject to an sparsity issue and an ovefitting issue in practice, both of which are successfully overcome by our BPGCN. Moreover, when combined with classic neural network methods, BPGCN yields extraordinary classification performances on some real-world datasets that have never been achieved before.

cond-mat.stat-mech↗