Related papers: Computational Frameworks for Patterned Two-Dimensi…
We study the nearest-neighbor spin-ice model subjected to a magnetic field applied along the global [111] and [110] directions, focusing on the role of sample geometry in stabilizing topological phase transitions. While no Kasteleyn…
We present a graph-based deep learning framework for predicting the magnetic properties of quasi-one-dimensional Ising spin systems. The lattice geometry is encoded as a graph and processed by a graph neural network (GNN) followed by fully…
Strongly-interacting nanomagnetic systems are pivotal across next-generation technologies including reconfigurable magnonics and neuromorphic computation. Controlling magnetisation state and local coupling between neighbouring nanoelements…
In this work we conduct a numerical search of non-trivial mechanisms, leading to new tendencies towards long-range ferromagnetic ordering in two-dimensional materials. For this purpose we employ an original variant of pairwise infinitesimal…
Microstructures, characterized by intricate structures at the microscopic scale, hold the promise of important disruptions in the field of mechanical engineering due to the superior mechanical properties they offer. One fundamental…
Neural models learn representations of high-dimensional data on low-dimensional manifolds. Multiple factors, including stochasticities in the training process, model architectures, and additional inductive biases, may induce different…
The fascinating realm of strain engineering and wetting transitions in two-dimensional (2D) materials takes place when placed on a two-dimensional array of nanopillars or one-dimensional rectangular grated substrates. Our investigation…
Strong coupling between optical and magnetic excitations could enable contactless, spatially resolved, or ultrafast interrogation and control of magnetism in two-dimensional (2D) materials and devices. The layered 2D A-type antiferromagnet…
Magnetic materials are typically described in terms of the Heisenberg model, which provides an accurate account of thermodynamic properties when combined with first principles calculations. This approach is usually based on an energy…
Two-dimensional (2D) van der Waals (vdW) materials show a range of profound physical properties that can be tailored through their incorporation in heterostructures and manipulated with external forces. The recent discovery of long-range…
Magnetic skyrmions, renowned for their fascinating electromagnetic properties, hold potential for next-generation topological spintronic devices. Recent advancements have unveiled a rich tapestry of 3D topological magnetism. Nevertheless,…
We present details of our effective computational methods based on the real-space finite-difference formalism to elucidate electronic and magnetic properties of the two-dimensional (2D) materials within the framework of the density…
The recent discovery of two-dimensional magnetic order in van-der Waals materials has stimulated a renaissance in the field of atomically-thin magnets. This has led to promising demonstrations of spintronic functionality such as tunneling…
Machine learning potentials have emerged as a powerful tool to extend the time and length scales of first principles-quality simulations. Still, most machine learning potentials cannot distinguish different electronic spin orientations and…
Magnetism in two-dimensional materials is of great importance in discovering new physical phenomena and developing new devices at the nanoscale. In this paper, first-principles simulations are used to calculate the electronic and magnetic…
Patterned magnetic nanostructures are advanced materials characterized by their unique magnetic properties at the nanoscale, which are the result of tailored geometric configurations and compositional engineering. As interest in…
We suggest a new mean field method for studying the thermodynamic competition between magnetic and superconducting phases in a two-dimensional square lattice. A partition function is constructed by writing microscopic interactions that…
Two-dimensional (2D) Transition Metal Dichalcogenide semiconductor (TMDs) nanocircuits are deterministically engineered over large-scale substrates. The original approach combines large-area physical growth of 2D TMDs layer with high…
Designing curvature in three-dimensional (3D) magnetic nanostructures enables controlled manipulation of local energy landscapes, allowing for the modification of noncollinear spin textures relevant for next-generation spintronic devices.…
Given the scarcity of experimentally confirmed magnetic structures, the reliable prediction of magnetic ground states is crucial; however, it remains a long-sought challenge because of the complex magnetic potential energy landscape. Here,…