Metal--Insulator Transitions in the Falicov--Kimball Model with Disorder

The ground state phase diagrams of the Falicov--Kimball model with local disorder is derived within the dynamical mean--field theory and using the geometrically averaged (''typical'') local density of states. Correlated metal, Mott insulator and Anderson insulator phases are identified. The metal--insulator transitions are found to be continuous. The interaction and disorder compete with each other stabilizing the metallic phase against occurring one of the insulators. The Mott and Anderson insulators are found to be continuously connected.Comment: 6 pages, 7 figure

Energy versus information based estimations of dissipation using a pair of magnetic colloidal particles

Using the framework of stochastic thermodynamics, we present an experimental study of a doublet of magnetic colloidal particles which is manipulated by a time-dependent magnetic field. Due to hydrodynamic interactions, each bead experiences a state-dependent friction, which we characterize using a hydrodynamic model. In this work, we compare two estimates of the dissipation in this system: the first one is energy based since it relies on the measured interaction potential, while the second one is information based since it uses only the information content of the trajectories. While the latter only offers a lower bound of the former, we find it to be simple to implement and of general applicability to more complex systems.Comment: Main text: 5 pages, 4 figures. Supplementary material: 5 pages, 5 figure

Magnetic properties of the three-dimensional Hubbard model at half filling

We study the magnetic properties of the 3d Hubbard model at half-filling in the TPSC formalism, previously developed for the 2d model. We focus on the N\'eel transition approached from the disordered side and on the paramagnetic phase. We find a very good quantitative agreement with Dynamical Mean-Field results for the isotropic 3d model. Calculations on finite size lattices also provide satisfactory comparisons with Monte Carlo results up to the intermediate coupling regime. We point out a qualitative difference between the isotropic 3d case, and the 2d or anisotropic 3d cases for the double occupation factor. Even for this local correlation function, 2d or anisotropic 3d cases are out of reach of DMF: this comes from the inability of DMF to account for antiferromagnetic fluctuations, which are crucial.Comment: RevTex, 9 pages +10 figure

The absence of finite-temperature phase transitions in low-dimensional many-body models: a survey and new results

After a brief discussion of the Bogoliubov inequality and possible generalizations thereof, we present a complete review of results concerning the Mermin-Wagner theorem for various many-body systems, geometries and order parameters. We extend the method to cover magnetic phase transitions in the periodic Anderson Model as well as certain superconducting pairing mechanisms for Hubbard films. The relevance of the Mermin-Wagner theorem to approximations in many-body physics is discussed on a conceptual level.Comment: 33 pages; accepted for publication as a Topical Review in Journal of Physics: Condensed Matte

Disorder-enhanced delocalization and local-moment quenching in a disordered antiferromagnet

The interplay of disorder and spin-fluctuation effects in a disordered antiferromagnet is studied. In the weak-disorder regime (W \le U), while the energy gap decreases rapidly with disorder, the sublattice magnetization, including quantum corrections, is found to remain essentially unchanged in the strong correlation limit. Magnon energies and Neel temperature are enhanced by disorder in this limit. A single paradigm of disorder-enhanced delocalization qualitatively accounts for all these weak disorder effects. Vertex corrections and magnon damping, which appear only at order (W/U)^4, are also studied. With increasing disorder a crossover is found at W \sim U, characterized by a rapid decrease in sublattice magnetization due to quenching of local moments, and formation of spin vacancies. The latter suggests a spin-dilution behavior, which is indeed observed in softened magnon modes, lowering of Neel temperature, and enhanced transverse spin fluctuations.Comment: 12 pages, includes 8 postscript figures. To appear in Physical Review B. References adde

Disorder and Impurities in Hubbard-Antiferromagnets

We study the influence of disorder and randomly distributed impurities on the properties of correlated antiferromagnets. To this end the Hubbard model with (i) random potentials, (ii) random hopping elements, and (iii) randomly distributed values of interaction is treated using quantum Monte Carlo and dynamical mean-field theory. In cases (i) and (iii) weak disorder can lead to an enhancement of antiferromagnetic (AF) order: in case (i) by a disorder-induced delocalization, in case (iii) by binding of free carriers at the impurities. For strong disorder or large impurity concentration antiferromagnetism is eventually destroyed. Random hopping leaves the local moment stable but AF order is suppressed by local singlet formation. Random potentials induce impurity states within the charge gap until it eventually closes. Impurities with weak interaction values shift the Hubbard gap to a density off half-filling. In both cases an antiferromagnetic phase without charge gap is observed.Comment: 16 pages, 9 figures, latex using vieweg.sty (enclosed); typos corrected, references updated; to appear in "Advances in Solid State Physics", Vol. 3

Magnetic Correlations in the Two Dimensional Anderson-Hubbard Model

The two dimensional Hubbard model in the presence of diagonal and off-diagonal disorder is studied at half filling with a finite temperature quantum Monte Carlo method. Magnetic correlations as well as the electronic compressibility are calculated to determine the behavior of local magnetic moments, the stability of antiferromagnetic long range order (AFLRO), and properties of the disordered phase. The existence of random potentials (diagonal or ``site'' disorder) leads to a suppression of local magnetic moments which eventually destroys AFLRO. Randomness in the hopping elements (off-diagonal disorder), on the other hand, does not significantly reduce the density of local magnetic moments. For this type of disorder, at half-filling, there is no ``sign-problem'' in the simulations as long as the hopping is restricted between neighbor sites on a bipartite lattice. This allows the study of sufficiently large lattices and low temperatures to perform a finite-size scaling analysis. For off-diagonal disorder AFLRO is eventually destroyed when the fluctuations of antiferromagnetic exchange couplings exceed a critical value. The disordered phase close to the transition appears to be incompressible and shows an increase of the uniform susceptibility at low temperatures.Comment: 10 pages, REVTeX, 14 figures included using psfig.st

Constrained-path quantum Monte Carlo simulations of the zero-temperature disordered two-dimensional Hubbard model

We study the effects of disorder on long-range antiferromagnetic correlations in the half-filled, two dimensional, repulsive Hubbard model at T=0. A mean field approach is first employed to gain a qualitative picture of the physics and to guide our choice for a trial wave function in a constrained path quantum Monte Carlo (CPQMC) method that allows for a more accurate treatment of correlations. Within the mean field calculation, we observe both Anderson and Mott insulating antiferromagnetic phases. There are transitions to a paramagnet only for relatively weak coupling, U < 2t in the case of bond disorder, and U < 4t in the case of on-site disorder. Using ground-state CPQMC we demonstrate that this mean field approach significantly overestimates magnetic order. For U=4t, we find a critical bond disorder of Vc = (1.6 +- 0.4)t even though within mean field theory no paramagnetic phase is found for this value of the interaction. In the site disordered case, we find a critical disorder of Vc = (5.0 +- 0.5)t at U=4t.Comment: Revtex, 13 pages, 15 figures. Minor changes to title and abstract, discussion and references added, figures 5, 6, 8, 9 replaced with easier to read version

Two-Particle-Self-Consistent Approach for the Hubbard Model

Even at weak to intermediate coupling, the Hubbard model poses a formidable challenge. In two dimensions in particular, standard methods such as the Random Phase Approximation are no longer valid since they predict a finite temperature antiferromagnetic phase transition prohibited by the Mermin-Wagner theorem. The Two-Particle-Self-Consistent (TPSC) approach satisfies that theorem as well as particle conservation, the Pauli principle, the local moment and local charge sum rules. The self-energy formula does not assume a Migdal theorem. There is consistency between one- and two-particle quantities. Internal accuracy checks allow one to test the limits of validity of TPSC. Here I present a pedagogical review of TPSC along with a short summary of existing results and two case studies: a) the opening of a pseudogap in two dimensions when the correlation length is larger than the thermal de Broglie wavelength, and b) the conditions for the appearance of d-wave superconductivity in the two-dimensional Hubbard model.Comment: Chapter in "Theoretical methods for Strongly Correlated Systems", Edited by A. Avella and F. Mancini, Springer Verlag, (2011) 55 pages. Misprint in Eq.(23) corrected (thanks D. Bergeron

Artificial Intelligence and Liver Transplant:Predicting Survival of Individual Grafts

The demand for liver transplantation far outstrips the supply of deceased donor organs, and so, listing and allocation decisions aim to maximize utility. Most existing methods for predicting transplant outcomes use basic methods, such as regression modeling, but newer artificial intelligence (AI) techniques have the potential to improve predictive accuracy. The aim was to perform a systematic review of studies predicting graft outcomes following deceased donor liver transplantation using AI techniques and to compare these findings to linear regression and standard predictive modeling: donor risk index (DRI), Model for End‐Stage Liver Disease (MELD), and Survival Outcome Following Liver Transplantation (SOFT). After reviewing available article databases, a total of 52 articles were reviewed for inclusion. Of these articles, 9 met the inclusion criteria, which reported outcomes from 18,771 liver transplants. Artificial neural networks (ANNs) were the most commonly used methodology, being reported in 7 studies. Only 2 studies directly compared machine learning (ML) techniques to liver scoring modalities (i.e., DRI, SOFT, and balance of risk [BAR]). Both studies showed better prediction of individual organ survival with the optimal ANN model, reporting an area under the receiver operating characteristic curve (AUROC) 0.82 compared with BAR (0.62) and SOFT (0.57), and the other ANN model gave an AUC ROC of 0.84 compared with a DRI (0.68) and SOFT (0.64). AI techniques can provide high accuracy in predicting graft survival based on donors and recipient variables. When compared with the standard techniques, AI methods are dynamic and are able to be trained and validated within every population. However, the high accuracy of AI may come at a cost of losing explainability (to patients and clinicians) on how the technology works