Related papers: Predicting magnetism with first-principles AI
Moir\'e materials provide an ideal platform for exploring quantum phases of matter. However, solving the many-electron problem in moir\'e systems is challenging due to strong correlation effects. We introduce a powerful variational…
Magnetic materials have been applied in a large variety of technologies, from data storage to quantum devices. The development of 2D materials has opened new arenas for magnetic compounds, even when classical theories discourage their…
Theoretical prediction of the 2nd-order magnetic transition temperature (TM) used to be arduous. Here, we develop a first principle-based, fully automatic structure-to-TM method for two-dimensional (2D) magnets whose effective Hamiltonians…
Monte Carlo simulations, in which the Schrodinger equation is solved at each Monte Carlo sweep, are employed to assess the influence of magnetization fluctuations,short-range antiferromagnetic interactions, disorder, magnetic polaron…
Magnetoelectric multiferroics are key materials for next-generation spintronic devices due to their entangled magnetic and ferroelectric properties. Spiral multiferroics possess ferroelectric polarization and are particularly promising for…
Recent discovery of several van der waals magnetic material and moire magnet introduce to us an extremely challenging and revolutionary era of 2D magnetism and correlated phenomena for low dimensional material.More often the simplest spin…
The S=1/2 Heisenberg chain with bond alternation and randomness of antiferromagnetic (AFM) and ferromagnetic (FM) interactions is investigated by quantum Monte Carlo simulations of loop/cluster algorithm. Our results have shown interesting…
The discovery of altermagnetism offers new opportunities for exploring novel quantum states and developing spintronic devices for enabling momentum dependent spin splitting in compensated systems, while zero net magnetization limit its…
Moir\'e patterns made of two-dimensional (2D) materials represent highly tunable electronic Hamiltonians, allowing a wide range of quantum phases to emerge in a single material. Current modeling techniques for moir\'e electrons requires…
Monolayer MnO$_2$ is one of the few predicted two-dimensional (2D) ferromagnets that has been experimentally synthesized and is commercially available. The Mermin-Wagner theorem states that magnetic order in a 2D material cannot persist…
This manuscript presents a model and simulation of the copper chalcopyrite semiconductor CuGaSe2 in order to predict its magnetic properties. In the semiconductor material CuGaSe2 (CGS), the atom Cu is the only magnetic element with a…
The structural and magnetic properties of functional Ni-Mn-Z (Z = Ga, In, Sn) Heusler alloys are studied by first-principles and Monte Carlo methods. The \textit{ab initio} calculations give a basic understanding of the underlying physics…
Motivated by the recent experimental developments in van der Waals heterostructures, we investigate the emergent magnetism in Mott insulator - semimetal moir\'e superlattices by deriving effective spin models and exploring their phase…
Altermagnetism, a new magnetic phase, has been theoretically proposed and experimentally verified to be distinct from ferromagnetism and antiferromagnetism. Although altermagnets have been found to possess many exotic physical properties,…
Accurately predicting magnetic behavior across diverse materials systems remains a longstanding challenge due to the complex interplay of structural and electronic factors and is pivotal for the accelerated discovery and design of…
We present a perspective on the status of antiferromagnetism in two-dimensional (2D) materials. Various types of spin-compensated orders are discussed and include non-collinear order, spin spirals and altermagnetism. Spin-orbit effects…
Combining material informatics and high-throughput electronic structure calculations offers the possibility of a rapid characterization of complex magnetic materials. Here we demonstrate that datasets of electronic properties calculated at…
We employ several unsupervised machine learning techniques, including autoencoders, random trees embedding, and t-distributed stochastic neighboring ensemble (t-SNE), to reduce the dimensionality of, and therefore classify, raw (auxiliary)…
As machine learning becomes increasingly important in engineering and science, it is inevitable that machine learning techniques will be applied to the investigation of materials, and in particular the structural phase transitions common in…
The effective spin Hamiltonian method is widely adopted to simulate and understand the behavior of magnetism. However, the magnetic interactions of some systems, such as itinerant magnets, are too complex to be described by any explicit…