Related papers: Machine learning dynamics of phase separation in c…
We apply unsupervised learning techniques to classify the different phases of the $J_1-J_2$ antiferromagnetic Ising model on the honeycomb lattice. We construct the phase diagram of the system using convolutional autoencoders. These neural…
The two-dimensional Kondo lattice model with both nearest and next-nearest neighbor exchange interactions is studied within a mean-field approach and its phase diagram is determined. In particular, we allow for lattice translation symmetry…
We consider a two dimensional Kondo lattice model with exchange J and hopping t in which three out of four impurity spins are removed in a regular way. At the particle-hole symmetric point the model may be studied with auxiliary field…
A Boltzmann machine is a stochastic neural network that has been extensively used in the layers of deep architectures for modern machine learning applications. In this paper, we develop a Boltzmann machine that is capable of modelling…
We study a bosonic version of the Kondo lattice model with an on-site repulsion in the conduction band, implemented with alkali atoms in two bands of an optical lattice. Using both weak and strong-coupling perturbation theory, we find that…
We classify phases of a bosonic lattice model based on the computational complexity of classically simulating the system. We show that the system transitions from being classically simulable to classically hard to simulate as it evolves in…
Employing the density matrix renormalization group method and strong-coupling perturbation theory, we study the phase diagram of the SU(2)xSU(2) Kondo lattice model in one dimension. We show that, at quarter filling, the system can exist in…
We present some preliminary results on the phase diagram of the 2D S=1/2 Kondo lattice model at finite doping. As a starting point the Hamiltonian is written in terms of local spin and charge excitations, and the interactions between these…
We study the quantum dynamics of a strongly correlated electron pair in a one-dimensional lattice, focusing on the occurrence of local dissociation/pairing mechanisms induced by a site energy defect. To this end, we simulate the time…
Machine learning has made important headway in helping to improve the treatment of quantum many-body systems. A domain of particular relevance are correlated inhomogeneous systems. What has been missing so far is a general, scalable…
Effective models focused on pertinent low-energy degrees of freedom have substantially contributed to our qualitative understanding of quantum materials. An iconic example, the Kondo model, was key to demonstrating that the rich phase…
A core problem in machine learning is to learn expressive latent variables for model prediction on complex data that involves multiple sub-components in a flexible and interpretable fashion. Here, we develop an approach that improves…
Fast and dynamical control of quantum phases is highly desired in the application of quantum phenomena in devices. On the experimental side, ultrafast laser pulses provide an ideal platform to induce femtosecond dynamics in a variety of…
The interaction of a lattice of localized magnetic moments with a sea of conduction electrons in Kondo lattice models induces rich quantum phases of matter, such as Fermi liquids with heavily renormalized electronic quasiparticles, quantum…
We study the effects of the Coulomb interaction in the one dimensional Kondo lattice model on the phase diagram, the static magnetic susceptibility and electron spin relaxation. We show that onsite Coulomb interaction supports ferromagnetic…
Recently, machine-learning methods have been shown to be successful in identifying and classifying different phases of the square-lattice Ising model. We study the performance and limits of classification and regression models. In…
At zero temperature, the Landauer formalism combined with static density functional theory is able to correctly reproduce the Kondo plateau in the conductance of the Anderson impurity model provided that an exchange-correlation potential is…
A description of phase separation kinetics for solid binary (A,B) mixtures in thin film geometry based on the Kawasaki spin-exchange kinetic Ising model is presented in a discrete lattice molecular field formulation. It is shown that the…
The Hofstadter-Hubbard model captures the physics of strongly correlated electrons in an applied magnetic field, which is relevant to many recent experiments on Moir\'e materials. Few large-scale, numerically exact simulations exists for…
Extensive Monte Carlo simulations are performed to analyze a recent neutron diffraction experiment on a distorted triangular lattice compound RbCoBr$_3$. We consider a spin-lattice model, where both spin and lattice are Ising variables.…