Search arXiv⌕ Search

arXiv · cond-mat/0306721

Spin-glass phase of cuprates

Abstract

We investigate a phenomenological model for the spin glass phase of La_{2-x}Sr_xCuO_4, in which it is assumed that holes doped into the CuO_2 planes localize near their Sr dopant, where they cause a dipolar frustration of the antiferromagnetic environment. In absence of long-range antiferromagnetic order, the spin system can reduce frustration, and also its free energy, by forming a state with an ordered orientation of the dipole moments, which leads to the appearance of spiral spin correlations. To investigate this model, a non-linear sigma model is used in which disorder is introduced via a randomly fluctuating gauge field. A renormalization group study shows that the collinear fixed point of the model is destroyed through the disorder and that the disorder coupling leads to an additive renormalization of the order parameter stiffness. Further, the stability of the spiral state against the formation of topological defects is investigated with the use of the replica trick. A critical disorder strength is found beyond which topological defects proliferate. Comparing our results with experimental data, it is found that for a hole density x > 0.02, i.e. in the entire spin glass regime, the disorder strength exceeds the critical threshold. In addition, some experiments are proposed in order to distinguish if the incommensurabilities observed in neutron scattering experiments correspond to a diagonal stripe or a spiral phase.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

N. Hasselmann, A. H. Castro Neto, C. Morais Smith. 2003-11-27. Spin-glass phase of cuprates. https://doi.org/10.1103/physrevb.69.014424

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Deep-layered machines have a built-in Occam's razor

Many complex systems map vast numbers of microscopic descriptions onto far fewer macroscopic outcomes. Surprisingly, these input-output maps strongly favor simple outputs. In an exactly solvable deep-layered machine, we prove that simplicity bias strengthens with every layer. The probability of an output function $F$ approaches $\Pr(F)\propto 2^{-K(F)}$, where $K$ is the Kolmogorov complexity. Our results show that hierarchical composition alone can generate an intrinsic Occam's razor, suggesting a general mechanism for simplicity bias in learning and evolution.

cond-mat.dis-nn↗

Neural Networks with Exciton-Polariton Waveguides

We propose and numerically study a scheme for neural networks using exciton-polariton waveguide meshes. These enable arbitrary programmable interlayer couplings suited to a restricted Boltzmann machine architecture where polariton phases act as stochastic network variables. We show that such a network can be trained using unsupervised Hebbian and anti-Hebbian updates. This allows the implementation of an associative memory, where the network recovers learned patterns from imperfect inputs.

cond-mat.dis-nn↗

Finite-width shift of the edge of chaos in deep neural networks

Deep neural networks train best when they are initialised at the edge of chaos, the critical line in the plane of weight and bias variances on which the distance between two inputs neither grows nor shrinks with depth. The line is known for infinitely wide networks; at finite width it moves, and how it moves has been disputed. We treat the logarithm of the distance between two inputs through a network of width $N$ as a random walk and compute its drift and variance to order $1/N$. The edge of chaos moves to a larger weight variance, by $Δσ_w^2=-a_1/(N\,\partial_{σ_w^2}\lnχ_1)$, about $8/N$ for $\tanh$ networks at a bias variance of $0.05$, confirmed by simulations of the exact finite-width recursion. The shift describes a typical network; the average over many networks is dominated by rare networks in which the distance grows, and its critical point moves the other way, which explains why calculations and simulations have disagreed. Backpropagation follows the same random walk, so the shift applies to gradients unchanged. Along the edge of chaos, the information about the similarity of two inputs is constant until the compression over the depth exceeds about one nat, which fixes where to sit on the line. The results give an initialisation rule, Crinit, that places a network on its finite-width edge of chaos without a search. On MNIST and Fashion-MNIST at width $64$ it is more accurate than the standard rules at their usual settings, by a small margin in shallow networks and a large one in deep networks, where most standard rules fail; it trains in fewer steps, and it matches or beats the standard rules with a tuned gain.

cond-mat.dis-nn↗