Publications

Order by: publication date number of citations

• Photoinduced pairing in Mott insulators

  Satoshi Ejima, Holger Fehske

  SciPost Phys. Proc. 11, 009 (2023) · published 5 June 2023 | Toggle abstract · pdf

  Utilizing time-evolution techniques in (infinite) matrix-product-state representation, we study the non-equilibrium dynamics of driven Mott insulators and demonstrate photoinduced $\eta$ pairing directly in the thermodynamic limit. Analyzing the time evolution of the corresponding pairing correlations, we determine the optimal laser pump parameters for which long-range $\eta$-pairing becomes dominant after pulse irradiation. The time-dependent photoemission spectra for this optimal pump parameter set show clear signatures of the photoinduced insulator-to-metal phase transition related to the formation of $\eta$ pairs.

• Electronic structure of CeAuAl$_3$ using density functional theory

  André Deyerling, Marc A. Wilde, Christian Pfleiderer

  SciPost Phys. Proc. 11, 008 (2023) · published 5 June 2023 | Toggle abstract · pdf

  We studied the magnetic properties and electronic structure of $CeAuAl_3$ using density functional theory. This compound shows a large Sommerfeld coefficient, a Kondo temperature, ${T_K=4\,K}$ and antiferromagnetic order below ${T_N=1.1\,K}$. We calculated the magnetic groundstate of ${CeAuAl_3}$ and the magnetic anisotropy energies. Treating the 4f-electrons as localized with DFT+U we obtain a good match with the magnetic properties observed experimentally. We also report salient features of the electronic structure of ${CeAuAl_3}$, including features of the Fermi surface and associated quantum oscillatory spectra, when the 4f-electrons are treated either as localized or itinerant.

• The two critical temperatures conundrum in La$_{1.83}$Sr$_{0.17}$CuO$_4$

  Abhisek Samanta, Itay Mangel, Amit Keren, Daniel P. Arovas, Assa Auerbach

  SciPost Phys. 16, 148 (2024) · published 5 June 2024 | Toggle abstract · pdf

  The in-plane and out-of-plane superconducting stiffness of ${\rm La}^{\vphantom{\dagger}}_{1.83}{\rm Sr}^{\vphantom{\dagger}}_{0.17}{\rm CuO}^{\vphantom{\dagger}}_4$ rings appear to vanish at different transition temperatures, which contradicts thermodynamical expectation. In addition, we observe a surprisingly strong dependence of the out-of-plane stiffness transition on sample width. With evidence from Monte Carlo simulations, this effect is explained by very small ratio $\alpha$ of inter-plane over intra-plane Josephson couplings. For three dimensional rings of millimeter dimensions, a crossover from layered three dimensional to quasi one dimensional behavior occurs at temperatures near the thermodynamic transition temperature ${T_{\rm c}}$, and the out-of-plane stiffness appears to vanish below ${T_{\rm c}}$ by a temperature shift of order $\alpha L_a/{\xi^{\parallel}}$, where $L_a/{\xi^{\parallel}}$ is the sample's width over coherence length. Including the effects of layer-correlated disorder, the measured temperature shifts can be fit by a value of $\alpha=4.1× 10^{-5}$, near ${T_{\rm c}}$, which is significantly lower than its previously measured value near zero temperature.

• Density-matrix mean-field theory

  Junyi Zhang, Zhengqian Cheng

  SciPost Phys. 17, 062 (2024) · published 23 August 2024 | Toggle abstract · pdf

  Mean-field theories have proven to be efficient tools for exploring diverse phases of matter, complementing alternative methods that are more precise but also more computationally demanding. Conventional mean-field theories often fall short in capturing quantum fluctuations, which restricts their applicability to systems with significant quantum effects. In this article, we propose an improved mean-field theory, density-matrix mean-field theory (DMMFT). DMMFT constructs effective Hamiltonians, incorporating quantum environments shaped by entanglements, quantified by the reduced density matrices. Therefore, it offers a systematic and unbiased approach to account for the effects of fluctuations and entanglements in quantum ordered phases. As demonstrative examples, we show that DMMFT can not only quantitatively evaluate the renormalization of order parameters induced by quantum fluctuations, but can also detect the topological quantum phases. Additionally, we discuss the extensions of DMMFT for systems at finite temperatures and those with disorders. Our work provides an efficient approach to explore phases exhibiting unconventional quantum orders, which can be particularly beneficial for investigating frustrated spin systems in high spatial dimensions.

• Catalogue of phonon modes in several cuprate high-temperature superconductors from density functional theory

  Noah J. Jabusch, Pavan Dayal, Alexander F. Kemper

  SciPost Phys. Core 6, 018 (2023) · published 23 March 2023 | Toggle abstract · pdf

  Cuprates are promising candidates for study in developing higher temperature superconductors. A thorough understanding of a material's phonon modes enables further investigation of its emergent properties, however, no complete reference of the phonon modes exists. Here, using density functional theory, we evaluate the phonon frequencies and atomic displacements for $\text{La}_2\text{CuO}_4$, $\text{Bi}_2\text{Sr}_2\text{CuO}_6$, and $\text{Bi}_2\text{Sr}_2\text{CaCu}_2\text{O}_8$, in their tetragonal structures. The phonon modes for all materials agree with those expected from space group symmetry and display instabilities corresponding to known low-temperature structural phase transitions.

• Rapid measurements and phase transition detections made simple by AC-GANs

  Jiewei Ding, Ho-Kin Tang, Wing Chi Yu

  SciPost Phys. Core 7, 035 (2024) · published 11 June 2024 | Toggle abstract · pdf

  In recent years, significant attention has been paid to using end-to-end neural networks for analyzing Monte Carlo data. However, the exploration of non-end-to-end generative adversarial neural networks remains limited. Here, we investigate classical many-body systems using generative adversarial neural networks. We employ the conditional generative adversarial network with an auxiliary classifier (AC-GAN) and integrate self-attention layers into the generator. This modification enables the network learn the distribution of the two-dimensional (2D) XY model's spin configurations and the physical quantities of interest. Utilizing the symmetry of the systems, we discover that AC-GAN can be trained with a very small raw dataset. This approach allows us to obtain reliable measurements for models typically demanding large samples, such as the large-sized 2D XY and the 3D constrained Heisenberg models. Moreover, we demonstrate the capability of AC-GAN to identify the phase transition points by quantifying the distribution changes in the spin configurations of the systems.