Related papers: Predicting magnetism with first-principles AI
We employ a combination of machine learning and first-principles calculations to predict magnetic properties of rare-earth lean magnets. For this purpose, based on training set constructed out of experimental data, the machine is trained to…
Nearest-neighbor Heisenberg antiferromagnet on a face-centered cubic lattice is studied by extensive Monte Carlo simulations in zero magnetic field. The parallel tempering algorithm is utilized, which allows to overcome a slow relaxation of…
Increased demand for high-performance permanent magnets in the electric vehicle and wind turbine industries has prompted the search for cost-effective alternatives.Discovering new magnetic materials with the desired intrinsic and extrinsic…
The magnetic properties of a material are determined by a subtle balance between the various interactions at play, a fact that makes the design of new magnets a daunting task. High-throughput electronic structure theory may help to explore…
Spin-ordered states close to metal-insulator transitions are poorly understood theoretically and challenging to probe in experiments. Here, we propose that the quantum twisting microscope, which provides direct access to the energy-momentum…
Monte Carlo simulations are performed for the S = 1/2 XY and ferro- and antiferromagnetic Heisenberg model in two dimensions using the loop algorithm. Thermodynamic properties of all these models are investigated in wide temperature range.…
We introduce a new family of trial wave-functions based on deep neural networks to solve the many-electron Schr\"odinger equation. The Pauli exclusion principle is dealt with explicitly to ensure that the trial wave-functions are physical.…
The dynamical responses of Ising metamagnet (layered antiferromagnet) in the presence of a sinusoidally oscillating magnetic field are studied by Monte Carlo simulation. The time average staggered magnetisation plays the role of dynamic…
The following electromagnetism (EM) inverse problem is addressed. It consists in estimating local radioelectric properties of materials recovering an object from the global EM scattering measurement, at various incidences and wave…
To design fast memory devices, we need material combinations which can facilitate fast read and write operation. We present a heterostructure comprising a two-dimensional (2D) magnet and a 2D topological insulator (TI) as a viable option…
Machine Learning (ML) plays an increasingly important role in the discovery and design of new materials. In this paper, we demonstrate the potential of ML for materials research using hard-magnetic phases as an illustrative case. We build…
Ferromagnetism emerges when the Moire superlattice formed by stacking two graphene monolayers in a magic twist angle are filled with integer number electrons. This work investigates the ferromagnetism based on the Ising models for a…
In intrinsic magnetic semiconductors, the absorption of a single photon can generate a spin polaron, whose magnetic moment reaches many thousands of Bohr magnetons [1.2]. Here we investigate photoinduced spin polarons, using Monte Carlo…
Resolving the interplay between magnetic interactions and structural properties in strongly correlated materials through a quantitatively accurate approach has been a major challenge in condensed matter physics. Here we apply highly…
The design of coordination compounds with target properties often requires years of continuous feedback loop between theory, simulations and experiments. In the case of magnetic molecules, this conventional strategy has indeed led to the…
The magnetoelectric effect and skyrmions are two fundamental phenomena in the field of condensed-matter physics. Here, using first-principles calculations and Monte-Carlo simulations, we propose that strong magnetoelectric coupling can be…
Two-dimensional materials and their heterostructures have opened up new possibilities for magnetism at the nanoscale. In this study, we utilize first-principles simulations to investigate the structural, electronic, and magnetic properties…
Two-dimensional moire superlattices have recently emerged as a fertile ground for creating novel electronic phases of matter with unprecedented control. Despite intensive efforts, theoretical investigation of correlated moire systems has…
We employed the recently developed density functional tight binding (DFTB) method's Hamiltonian, GFN1-xTB, for modeling the mixed termination in Ti$_{{\rm2}}$C MXene, namely three types of termination by combining -O and -OH, -O and -F, as…
The exploration of quantum phases in moir\'e systems has drawn intense experimental and theoretical efforts. The realization of honeycomb symmetry has been a recent focus. The combination of strong interaction and honeycomb symmetry can…