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Register a new userMachine Learning Identification of Impurities in the STM Images
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In this work we train a neural network to identify impurities in the experimental images obtained by the scanning tunneling microscope measurements. The neural network is first trained with large number of simulated data and then the trained neural network is applied to identify a set of experimental images taken at different voltages. We use the convolutional neural network to extract features from the images and also implement the attention mechanism to capture the correlations between images taken at different voltages. We note that the simulated data can capture the universal Friedel oscillation but cannot properly describe the non-universal physics short-range physics nearby an impurity, as well as noises in the experimental data. And we emphasize that the key of this approach is to properly deal these differences between simulated data and experimental data. Here we show that even by including uncorrelated white noises in the simulated data, the performance of neural network on experimental data can be significantly improved. To prevent the neural network from learning unphysical short-range physics, we also develop another method to evaluate the confidence of the neural network prediction on experimental data and to add this confidence measure into the loss function. We show that adding such an extra loss function can also improve the performance on experimental data. Our research can inspire future similar applications of machine learning on experimental data analysis.
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Quasiparticles are physically motivated mathematical constructs for simplifying the seemingly complicated many-body description of solids. A complete understanding of their dynamics and the nature of the effective interactions between them provides rich information on real material properties at the microscopic level. In this work, we explore the dynamics and interactions of magnon quasiparticles in a ferromagnetic spin-1 Heisenberg chain with easy-axis onsite anisotropy, a model relevant for the explanation of recent terahertz optics experiments on NiNb$_2$O$_6$ [P. Chauhan et al., Phys. Rev. Lett. 124, 037203 (2020)], and nonequilibrium dynamics in ultra cold atomic settings [W.C. Chung et al., Phys. Rev. Lett. 126, 163203 (2021)]. We build a picture for the properties of clouds of few magnons with the help of exact diagonalization and density matrix renormalization group calculations supported by physically motivated Jastrow wavefunctions. We show how the binding energy of magnons effectively reduces with their number and explain how this energy scale is of direct relevance for dynamical magnetic susceptibility measurements. This understanding is used to make predictions for ultra cold-atomic platforms which are ideally suited to study the thermalization of multimagnon states. We simulate the non-equilibrium dynamics of these chains using the matrix product state based time-evolution block decimation algorithm and explore the dependence of revivals and thermalization on magnon density and easy-axis onsite anisotropy (which controls the strength of effective magnon interactions). We observe behaviors akin to those reported for many-body quantum scars which we explain with an analytic approximation that is accurate in the limit of small anisotropy.
Semiconductor valence holes are known to have heavy and light effective masses; but the consequence of this mass difference on Coulomb scatterings has been considered intractable and thus ignored up to now. The reason is that the heavy/light index is quantized along the hole momentum that changes in a Coulomb scattering; so, a heavy hole can turn light, depending on the scattering angle. This mass change has never been taken into account in many-body problems, and a single ``average hole mass has been used instead. In order to study the missed consequences of this crude approximation, the first necessary step is to determine the Coulomb scatterings with valence holes in a precise way. We here derive these scatterings from scratch, starting from the threefold valence-electron spatial level, all the way through the spin-orbit splitting, the Kohn-Luttinger effective Hamiltonian, its spherical approximation, and the phase factors that appear when turning from valence electron to hole operators, that is, all the points of semiconductor physics that render valence holes so different from a na{i}ve positive charge.
A spin-1 Heisenberg model on trimerized Kagome lattice is studied by doing a low-energy bosonic theory in terms of plaquette-triplons defined on its triangular unit-cells. The model has an intra-triangle antiferromagnetic exchange interaction, $J$ (set to 1), and two inter-triangle couplings, $J^prime>0$ (nearest-neighbor) and $J^{primeprime}$ (next-nearest-neighbor; of both signs). The triplon analysis of this model studies the stability of the trimerized singlet (TS) ground state in the $J^prime$-$J^{primeprime}$ plane. It gives a quantum phase diagram that has two gapless antiferromagnetically (AF) ordered phases separated by the spin-gapped TS phase. The TS ground state is found to be stable on $J^{primeprime}=0$ line (the nearest-neighbor case), and on both sides of it for $J^{primeprime} eq 0$, in an extended region bounded by the critical lines of transition to the gapless AF phases. The gapless phase in the negative $J^{primeprime}$ region has a $sqrt{3}timessqrt{3}$ coplanar $120^circ$-AF order, with all the moments of equal length and relative angles of $120^circ$. The other AF phase, in the positive $J^{primeprime}$ region, is found to exhibit a different coplanar order with ordering wave vector ${bf q}=(0,0)$. Here, two magnetic moments in a triangle are of same magnitude, but shorter than the third. While the angle between the two short moments is $120^circ-2delta$, it is $120^circ+delta$ between a short and the long one. Only when $J^{primeprime}=J^prime$, their magnitudes become equal and the relative-angles $120^circ$. This ${bf q}=(0,0)$ phase has the translational symmetry of the Kagome lattice with isosceles triangular unit-cells. The ratio of the intensities of certain Bragg peaks, $I_{(1,0)}/I_{(0,1)} = 4sin^2{(frac{pi}{6}+delta)}$, presents an experimental measure of the deviation, $delta$, from the $120^circ$ order.
Hidden order in URu$_2$Si$_2$ has remained a mystery now entering its 4th decade. The importance of resolving the nature of the hidden order has stimulated extensive research. Here we present a detailed characterization of different surface terminations in URu$_2$Si$_2$ by angle-resolved photoemission spectroscopy, in conjunction with scanning tunneling spectroscopy and DMFT calculations that may unveil a new piece of this puzzle. The U-terminated surface is characterized by an electron-like band around the $bar{X}$ point, while a hole-like band for the Si-terminated surface. We also investigate temperature evolution of the electronic structure around the $bar{X}$ point from 11 K up to 70 K, and did not observe any abrupt change of the electronic structure around the coherence temperature (55 K). The $f$ spectral weight gradually weakens upon increasing temperature, still some $f$ spectral weight can be found above this temperature. Our results suggest that surface terminations in URu$_2$Si$_2$ are an important issue to be taken into account in future work.
In this paper, we apply machine learning methods to study phase transitions in certain statistical mechanical models on the two dimensional lattices, whose transitions involve non-local or topological properties, including site and bond percolations, the XY model and the generalized XY model. We find that using just one hidden layer in a fully-connected neural network, the percolation transition can be learned and the data collapse by using the average output layer gives correct estimate of the critical exponent $ u$. We also study the Berezinskii-Kosterlitz-Thouless transition, which involves binding and unbinding of topological defects---vortices and anti-vortices, in the classical XY model. The generalized XY model contains richer phases, such as the nematic phase, the paramagnetic and the quasi-long-range ferromagnetic phases, and we also apply machine learning method to it. We obtain a consistent phase diagram from the network trained with only data along the temperature axis at two particular parameter $Delta$ values, where $Delta$ is the relative weight of pure XY coupling. Besides using the spin configurations (either angles or spin components) as the input information in a convolutional neural network, we devise a feature engineering approach using the histograms of the spin orientations in order to train the network to learn the three phases in the generalized XY model and demonstrate that it indeed works. The trained network by using system size $Ltimes L$ can be used to the phase diagram for other sizes ($Ltimes L$, where $L e L$) without any further training.
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