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The use of topology optimization methods for the design of electric machines has become increasingly popular over the past years. Due to a desired increase in power density and a recent trend to high speed machines, thermal aspects play a…

Optimization and Control · Mathematics 2026-04-01 Peter Gangl , Nepomuk Krenn , Herbert De Gersem

We present a multi-scale computational approach that combines atomistic spin models with the cluster multipole (CMP) method. The CMP method enables a systematic and accurate generation of complex non-collinear magnetic structures using…

Materials Science · Physics 2025-03-06 Juba Bouaziz , Takuya Nomoto , Ryotaro Arita

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…

Strongly Correlated Electrons · Physics 2026-04-29 Agustin Medina , Marcelo Arlego , Carlos A. Lamas

We propose a non-collinear spin-constrained method that generates training data for deep-learning-based magnetic model, which provides a powerful tool for studying complex magnetic phenomena that requires large-scale simulations at the…

An empirical multiorbital (spd) tight binding (TB) model including magnetism and spin-orbit coupling is applied to calculations of magnetic anisotropy energy (MAE) in CoPt L1_0 structure. A realistic Slater-Koster parametrisation for…

Materials Science · Physics 2014-03-05 J. Zemen , J. Mašek , J. Kučera , J. A. Mol , P. Motloch , T. Jungwirth

We propose a method for efficiently coupling the finite element method with atomistic simulations, while using molecular dynamics or kinetic Monte Carlo techniques. Our method can dynamically build an optimized unstructured mesh that…

Computational Engineering, Finance, and Science · Computer Science 2018-05-23 Mihkel Veske , Andreas Kyritsakis , Kristjan Eimre , Vahur Zadin , Alvo Aabloo , Flyura Djurabekova

In this experience report, we apply deep active learning to the field of design optimization to reduce the number of computationally expensive numerical simulations. We are interested in optimizing the design of structural components, where…

Machine Learning · Computer Science 2024-03-21 Jens Decke , Christian Gruhl , Lukas Rauch , Bernhard Sick

We present a method for optimising experimental cuts in order to place the strongest constraints (upper limits) on theoretical signal models. The method relies only on signal and background expectations derived from Monte-Carlo simulations,…

Astrophysics · Physics 2009-11-07 Gary C. Hill , Katherine Rawlins

The description of the behavior of a material subjected to multi-physics loadings requires the formulation of constitutive laws that usually derive from Gibbs free energies, using invariant quantities depending on the considered physics and…

Computational Engineering, Finance, and Science · Computer Science 2023-06-14 Julien Taurines , Boris Kolev , Rodrigue Desmorat , Olivier Hubert

We carry out highly accurate \emph{ab initio} path integral Monte Carlo (PIMC) simulations to directly estimate the free energy of various warm dense matter systems including the uniform electron gas and hydrogen without any nodal…

Quantum Gases · Physics 2024-07-02 Tobias Dornheim , Zhandos Moldabekov , Sebastian Schwalbe , Jan Vorberger

In fusion reactor design, steels under consideration for the blanket are ferromagnetic, so the steel's effect on the plasma physics must be examined. For efficient calculation of these fields, we can exploit the fact that the magnetic…

Plasma Physics · Physics 2026-01-13 Matt Landreman , Humberto Torreblanca , Antoine Cerfon

The advancement of machine learning technologies has revolutionized the search and optimization of material properties. These algorithms often rely on theoretical calculations, such as density functional theory (DFT), for data inputs and…

Materials Science · Physics 2024-11-06 Christopher Broyles , William Charles , Sheng Ran

There exists a significant challenge in developing efficient magnetic tunnel junctions with low write currents for non-volatile memory devices. With the aim of analysing potential materials for efficient current-operated magnetic junctions…

Mesoscale and Nanoscale Physics · Physics 2017-12-13 Matthew O. A. Ellis , Maria Stamenova , Stefano Sanvito

Spin wave computing device where an algorithm can be encoded by recording a corresponding magnetization pattern onto a hard magnetic material was previously proposed1 and a particular implementation of a vector-matrix algorithm was…

Applied Physics · Physics 2023-06-16 Kirill Rivkin

The finite-element analysis of three-dimensional magnetostatic problems in terms of magnetic vector potential has proven to be one of the most efficient tools capable of providing the excellent quality results but becoming computationally…

Computational Physics · Physics 2023-07-25 Alexander Chervyakov

Atomistic simulations of thermodynamic properties of magnetic materials rely on an accurate modelling of magnetic interactions and an efficient sampling of the high-dimensional spin space. Recent years have seen significant progress with a…

Statistical Mechanics · Physics 2019-03-27 Ning Wang , Thomas Hammerschmidt , Jutta Rogal , Ralf Drautz

Numerically exact investigations of interacting spin systems provide a major tool for an understanding of their magnetic properties. For medium size systems the approximate Lanczos diagonalization is the most common method. In this article…

Strongly Correlated Electrons · Physics 2008-06-02 J. Schnack , P. Hage , H. -J. Schmidt

A generalised extraction procedure for magnetic interactions using effective Hamiltonians is presented that is applicable to systems with more than two sites featuring local spins $S_i \geq 1$. To this end, closed, non-recursive expressions…

Chemical Physics · Physics 2025-06-03 Arta A. Safari , Nikolay A. Bogdanov

We demonstrate the feasibility of performing sufficient configurational sampling of disordered oxides directly from first principles without resorting to the use of fitted models such as cluster expansion. This is achieved by harnessing the…

Materials Science · Physics 2019-01-30 Shusuke Kasamatsu , Osamu Sugino

We present our calculation results for organic magnetic electrides. In order to identify the `cavity' electrons, we use maximally-localized Wannier functions and `empty atom' technique. The estimation of magnetic coupling is then performed…

Materials Science · Physics 2018-06-28 Taek Jung Kim , Hongkee Yoon , Myung Joon Han