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The problem of the linear microtearing mode in a slab magnetised plasma, and its connection to kinetic reconnecting modes, is addressed. Electrons are described using a novel hybrid fluid-kinetic model that captures electron heating, ions…
In this work, we study the effects of a transverse magnetic field in a Kondo lattice model with two $f$ orbitals interacting with the conduction electrons. The $f$ electrons that are present on the same site interact through Hund's…
Motivated by recent experiments on molecular quantum dots we investigate the relaxation of pure spin states when coupled to metallic leads. Under suitable conditions these systems are well described by a ferromagnetic Kondo model. Using two…
We propose a hierarchy of multi-level kinetic Monte Carlo methods for sampling high-dimensional, stochastic lattice particle dynamics with complex interactions. The method is based on the efficient coupling of different spatial resolution…
We investigate the efficient learning of magnetic phases using artificial neural networks trained on synthetic data, combining computational simplicity with physics-informed strategies. Focusing on the diluted Ising model, which lacks an…
We investigate the reconstruction of a Fermi surface, which is called a Lifshitz transition, in magnetically ordered phases of the periodic Anderson model on a square lattice with a finite Coulomb interaction between f electrons. We apply…
The phase diagram of the Kondo lattice Hamiltonian with ferromagnetic Hund's coupling in the limit where the spin of the localized $t_{2g}$ electrons is classical is analyzed in one dimension as a function of temperature, electronic…
We study the competition between different possible ground states of the double-exchange model with strong ferromagnetic exchange interaction between itinerant electrons and local spins. Both for classical and quantum treatment of the local…
Machine learning methods are powerful in distinguishing different phases of matter in an automated way and provide a new perspective on the study of physical phenomena. We train a Restricted Boltzmann Machine (RBM) on data constructed with…
The influence of local electronic correlations on the properties of colossal magnetoresistance manganites is investigated. To this end, a ferromagnetic two-band Kondo lattice model is supplemented with the local Coulomb repulsion missing in…
The purpose of this article is to present a detailed numerical study of the second-order phase transition in the 2D Ising model. The importance of correctly presenting elementary theory of phase transitions, computational algorithms and…
The magnetic properties of a system of coexisting localized spins and conduction electrons are investigated within an extended version of the one dimensional Kondo lattice model in which effects stemming from the electron-lattice and…
Electronic flat bands represent a paradigmatic platform to realize strongly correlated matter due to their associated divergent density of states. In common instances, including electron-electron interactions leads to magnetic instabilities…
We employ the "learning by confusion" technique, an unsupervised machine learning approach for detecting phase transitions, to analyze quantum Monte Carlo simulations of the two-dimensional Holstein model--a fundamental model for…
We present an new approach for the ferromagnetic, three-dimensional, translational-symmetric Kondo lattice model which allows us to derive both magnon energies and linewidths (lifetimes) and to study the properties of the ferromagnetic…
Understanding mechanisms of quantum ordering in strongly correlated systems remains a central challenge in condensed matter physics, with implications for designing novel quantum materials. Here, we investigate kinetic antiferromagnetism on…
Machine learning techniques not only offer efficient tools for modelling dynamical systems from data, but can also be employed as frontline investigative instruments for the underlying physics. Nontrivial information about the original…
We explore the phase diagram of two-component bosons with Feshbach resonant pairing interactions in an optical lattice. It has been shown in previous work to exhibit a rich variety of phases and phase transitions, including a paradigmatic…
The incorporation of appropriate inductive bias plays a critical role in learning dynamics from data. A growing body of work has been exploring ways to enforce energy conservation in the learned dynamics by encoding Lagrangian or…
The Kondo lattice model introduced in 1977 describes a lattice of localized magnetic moments interacting with a sea of conduction electrons. It is one of the most important canonical models in the study of a class of rare earth compounds,…