Effect of long-range hopping on Tc in a two-dimensional Hubbard-Holstein model of the cuprates

We study the effect of long-range hoppings on Tc for the two-dimensional (2D) Hubbard model with and without Holstein phonons using parameters evaluated from band-structure calculations for cuprates. Employing the dynamical cluster approximation (DCA) with a quantum Monte Carlo (QMC) cluster solver for a 4-site cluster, we observe that without phonons, the long-range hoppings, t' and t'', generally suppress Tc. We argue that this trend remains valid for larger clusters. In the presence of the Holstein phonons, a finite t' enhances Tc in the under-doped region for the hole-doped system, consistent with local-density approximation (LDA) calculations and experiment. This is interpreted through the suppression of antiferromagnetic (AF) correlations and the interplay between polaronic effects and the antiferromagnetism.Comment: 5 pages, 4 figure

Short-Range Correlations and Cooling of Ultracold Fermions in the Honeycomb Lattice

We use determinantal quantum Monte Carlo simulations and numerical linked-cluster expansions to study thermodynamic properties and short-range spin correlations of fermions in the honeycomb lattice. We find that, at half filling and finite temperatures, nearest-neighbor spin correlations can be stronger in this lattice than in the square lattice, even in regimes where the ground state in the former is a semimetal or a spin liquid. The honeycomb lattice also exhibits a more pronounced anomalous region in the double occupancy that leads to stronger adiabatic cooling than in the square lattice. We discuss the implications of these findings for optical lattice experiments.Comment: 5 pages, 4 figure

Quantum Criticality and Incipient Phase Separation in the Thermodynamic Properties of the Hubbard Model

Transport measurements on the cuprates suggest the presence of a quantum critical point hiding underneath the superconducting dome near optimal hole doping. We provide numerical evidence in support of this scenario via a dynamical cluster quantum Monte Carlo study of the extended two-dimensional Hubbard model. Single particle quantities, such as the spectral function, the quasiparticle weight and the entropy, display a crossover between two distinct ground states: a Fermi liquid at low filling and a non-Fermi liquid with a pseudogap at high filling. Both states are found to cross over to a marginal Fermi-liquid state at higher temperatures. For finite next-nearest-neighbor hopping t' we find a classical critical point at temperature T_c. This classical critical point is found to be associated with a phase separation transition between a compressible Mott gas and an incompressible Mott liquid corresponding to the Fermi liquid and the pseudogap state, respectively. Since the critical temperature T_c extrapolates to zero as t' vanishes, we conclude that a quantum critical point connects the Fermi-liquid to the pseudogap region, and that the marginal-Fermi-liquid behavior in its vicinity is the analogous of the supercritical region in the liquid-gas transition.Comment: 18 pages, 9 figure

Thermodynamics of the Quantum Critical Point at Finite Doping in the 2D Hubbard Model: A Dynamical Cluster Approximation Study

We study the thermodynamics of the two-dimensional Hubbard model within the dynamical cluster approximation. We use continuous time quantum Monte Carlo as a cluster solver to avoid the systematic error which complicates the calculation of the entropy and potential energy (double occupancy). We find that at a critical filling, there is a pronounced peak in the entropy divided by temperature, S/T, and in the normalized double occupancy as a function of doping. At this filling, we find that specific heat divided by temperature, C/T, increases strongly with decreasing temperature and kinetic and potential energies vary like T^2 ln(T). These are all characteristics of quantum critical behavior.Comment: 4 pages, 4 figures. Submitted to Phys. Rev. B Rapid Communications on June 27, 200

A perspective on machine learning and data science for strongly correlated electron problems

Numerical approaches to the correlated electron problem have achieved considerable success, yet are still constrained by several bottlenecks, including high order polynomial or exponential scaling in system size, long autocorrelation times, challenges in recognizing novel phases, and the Fermion sign problem. Methods in machine learning (ML), artificial intelligence, and data science promise to help address these limitations and open up a new frontier in strongly correlated quantum system simulations. In this paper, we review some of the progress in this area. We begin by examining these approaches in the context of classical models, where their underpinnings and application can be easily illustrated and benchmarked. We then discuss cases where ML methods have enabled scientific discovery. Finally, we will examine their applications in accelerating model solutions in state-of-the-art quantum many-body methods like quantum Monte Carlo and discuss potential future research directions

Magnetic Correlations and Pairing in the 1/5-Depleted Square Lattice Hubbard Model

We study the single-orbital Hubbard model on the 1/5-depleted square-lattice geometry, which arises in such diverse systems as the spin-gap magnetic insulator CaV4O9 and ordered-vacancy iron selenides, presenting new issues regarding the origin of both magnetic ordering and superconductivity in these materials. We find a rich phase diagram that includes a plaquette singlet phase, a dimer singlet phase, a Néel and a block-spin antiferromagnetic phase, and stripe phases. Quantum Monte Carlo simulations show that the dominant pairing correlations at half filling change character from d wave in the plaquette phase to extended s wave upon transition to the Néel phase. These findings have intriguing connections to iron-based superconductors, and suggest that some physics of multiorbital systems can be captured by a single-orbital model at different dopings

Application of the Adjusted Weak Axiom of Profit Maximization to New Zealand Dairy Farming

The weak axiom of profit maximization is a nonparametric, empirical approach that has been used in the United States to analyze dairy farmers’ production and profit behavior under input and output price changes to determine whether farmers effectively respond to these changes. The expectation is that profit calculated using the current year’s input and output combination will be greater than that calculated from the previous year’s combination with current prices more often than due to chance. This approach was replicated using New Zealand dairy farm data (1,785 pairs of records over five years). Current year’s profits were significantly greater in two of the years and less in two years and in total. New Zealand’s pasture-based systems mean that this approach has limitations in evaluating farmers’ input and output decisions in response to price changes. Factors such as climatic impacts on pasture availability (a volatile input not included in the data set), and hence purchased feed requirements, affected the results. Farmer responses to costs and prices were not readily differentiated from other factors that affected input decisions or output. Results were interpreted with respect to climate, production, and income and cost changes, both nationally and regionally, with some interesting observations on farmer responses to variability

Case Notes

For decades, optical time-domain searches have been tuned to find ordinary supernovae, which rise and fall in brightness over a period of weeks. Recently, supernova searches have improved their cadences and a handful of fast-evolving luminous transients have been identified(1-5). These have peak luminosities comparable to type Ia supernovae, but rise to maximum in less than ten days and fade from view in less than one month. Here we present the most extreme example of this class of object thus far: KSN 2015K, with a rise time of only 2.2 days and a time above half-maximum of only 6.8 days. We show that, unlike type Ia supernovae, the light curve of KSN 2015K was not powered by the decay of radioactive elements. We further argue that it is unlikely that it was powered by continuing energy deposition from a central remnant (a magnetar or black hole). Using numerical radiation hydrodynamical models, we show that the light curve of KSN 2015K is well fitted by a model where the supernova runs into external material presumably expelled in a pre-supernova mass-loss episode. The rapid rise of KSN 2015K therefore probes the venting of photons when a hypersonic shock wave breaks out of a dense extended medium.NASA NNH15ZDA001N NNX17AI64G Australian Research Council Centre of Excellence for All-sky Astrophysics CE11000102