arXiv subjects

Johann Fischbacher

Publications and source records attributed to Johann Fischbacher.

At least 19 recordsLinked to original sources

arXiv2026

Tailoring the material properties, nanostructure and grain alignment of Alnico magnets through micromagnetic simulations

Alnico magnets have gained renewed interest in the search for rare-earth free permanent magnets due to their high thermal stability and magnetisation. However, the limited coercivity of these shape-anisotropy-based alloys constrains their performance. Starting from a reference Alnico sample, we realised a finite elements micromagnetic study of exchange-decoupled rods by varying their dimensions and interrod spacing across those observed experimentally. We computed the hysteresis properties by progressing from micromagnetic simulations of a small number of rods within the magnetostatic field of their neighbours to large systems treated statistically based on the distribution of orientations of the grains. We compared the coercivity of an isolated rod with that of the exchange-decoupled system to highlight the effect of magnetostatic interactions. We computed analytically the stray field acting on a single rod as a consequence of its surrounding rods in order to confirm the scaling of the coercivity with the packing fraction p. We explored how intrinsic material properties influence magnetic behaviour by examining materials with different magnetocrystalline anisotropy constants and saturation polarisation values. Results from several hundred simulations were used to train a multi-layer perceptron regressor and predict the magnetic properties as function of the dimensions of the rods, interrod spacing and orientation of the grains. With this approach, we highlight the underlying trends by which nanoscale structuring, intrinsic material properties and grain alignment can be tailored to improve the magnetic properties of Alnico alloys.

cond-mat.mtrl-sci↗

arXiv2025

Graph Neural Networks to Predict Coercivity of Hard Magnetic Microstructures

Graph neural networks (GNN) are a promising tool to predict magnetic properties of large multi-grain structures, which can speed up the search for rare-earth free permanent magnets. In this paper, we use our magnetic simulation data to train a GNN to predict coercivity of hard magnetic microstructures. We evaluate the performance of the trained GNN and quantify its uncertainty. Subsequently, we reuse the GNN architecture for predicting the maximum energy product. Out-of-distribution predictions of coercivity are also performed, following feature engineering based on the observed dependence of coercivity on system size.

physics.comp-ph↗

arXiv2022

FORC Diagram Features of Co Particles due to Reversal by Domain Nucleation

First Order Reversal Curve (FORC) diagrams are a popular tool in geophysics and materials science for the characterization of magnetic particles of natural and synthetic origin. However, there is still a lot of controversy about the rigorous interpretation of the origin of certain features in a FORC diagram. In this study, we analyze FORCs computed by micromagnetic simulations of Co cubes with dimensions of 50, 100 and 150 nm and uniaxial magnetocrystalline anisotropy. For the larger cubes we observe the formation of a stable two-domain state. The nucleation of a reversed domain and its subsequent annihilation are clearly visible as separate peaks in the FORC diagram. They spread out along the coordinate axis in the FORC diagram, which is associated with the bias field $H_U$ of a Preisach hysteron. Based on our findings, we state that a FORC diagram peak spreading along the $H_U$ axis can have its origin in the step-wise magnetization reversal driven by nucleation of domains in a single particle. This means that we have identified another mechanism apart from the well-known magnetostatic interaction between a set of particles that leads to features in the FORC diagram extending along the $H_U$-axis. Our study demonstrates that if FORCs shall be used as a quantitative tool to assess the microstructure of samples containing magnetic material, more information from other methods will be required to identify the correct physical mechanism by which a certain "fingerprint" in a FORC diagram is produced.

cond-mat.other↗

arXiv2022

Description of collective magnetization processes with machine learning models

This work introduces a latent space method to calculate the demagnetization reversal process of multigrain permanent magnets. The algorithm consists of two deep learning models based on neural networks. The embedded Stoner-Wohlfarth method is used as a reduced order model for computing samples to train and test the machine learning approach. The work is a proof of concept of the method since the used microstructures are simple and the only varying parameters are the magnetic anisotropy axes of the hard magnetic grains. A convolutional autoencoder is used for nonlinear dimensionality reduction, in order to reduce the required amount of training samples for predicting the hysteresis loops. We enriched the loss function with physical information about the underlying problem which increases the accuracy of the machine learning approach. A deep learning regressor is operating in the latent space of the autoencoder. The predictor takes a series of previously encoded magnetization states and outputs the next magnetization state along the hysteresis curve. To predict the complete demagnetization loop, we apply a recursive learning scheme.

physics.comp-ph↗

arXiv2022

Exploring the hysteresis properties of nanocrystalline permanent magnets using deep learning

We demonstrate the use of model order reduction and neural networks for estimating the hysteresis properties of nanocrystalline permanent magnets from microstructure. With a data-driven approach, we learn the demagnetization curve from data-sets created by grain growth and micromagnetic simulations. We show that the granular structure of a magnet can be encoded within a low-dimensional latent space. Latent codes are constructed using a variational autoencoder. The mapping of structure code to hysteresis properties is a multi-target regression problem. We apply deep neural network and use parameter sharing, in order to predict anchor points along the demagnetization curves from the magnet's structure code. The method is applied to study the magnetic properties of nanocrystalline permanent magnets. We show how new grain structures can be generated by interpolation between two points in the latent space and how the magnetic properties of the resulting magnets can be predicted.

cond-mat.mtrl-sci↗

arXiv2021

Full-Spin-Wave-Scaled Finite Element Stochastic Micromagnetism: Mesh-Independent FUSSS LLG Simulations of Ferromagnetic Resonance and Reversal

In this paper, we address the problem that standard stochastic Landau-Lifshitz-Gilbert (sLLG) simulations typically produce results that show unphysical mesh-size dependence. The root cause of this problem is that the effects of spin wave fluctuations are ignored in sLLG. We propose to represent the effect of these fluctuations by a "FUll-Spinwave-Scaled Stochastic LLG", or FUSSS LLG method. In FUSSS LLG, the intrinsic parameters of the sLLG simulations are first scaled by scaling factors that integrate out the spin wave fluctuations up to the mesh size, and the sLLG simulation is then performed with these scaled parameters. We developed FUSSS LLG by studying the Ferromagnetic Resonance (FMR) in Nd$_2$Fe$_{14}$B cubes. The nominal scaling greatly reduced the mesh size dependence relative to sLLG. We further discovered that adjusting one scaling exponent by less than 10% delivered fully mesh-size-independent results for the FMR peak. We then performed three tests and validations of our FUSSS LLG with this modified scaling. 1) We studied the same FMR but with magnetostatic fields included. 2) We simulated the total magnetization of the Nd$_2$Fe$_{14}$B cube. 3) We studied the effective, temperature- and sweeping rate-dependent coercive field of the cubes. In all three cases we found that FUSSS LLG delivered essentially mesh-size-independent results, which tracked the theoretical expectations better than unscaled sLLG. Motivated by these successful validations, we propose that FUSSS LLG provides marked, qualitative progress towards accurate, high precision modeling of micromagnetics in hard, permanent magnets.

physics.comp-ph↗

arXiv2021

Magnetostatics and micromagnetics with physics informed neural networks

Partial differential equations and variational problems can be solved with physics informed neural networks (PINNs). The unknown field is approximated with neural networks. Minimizing the residuals of the static Maxwell equation at collocation points or the magnetostatic energy, the weights of the neural network are adjusted so that the neural network solution approximates the magnetic vector potential. This way, the magnetic flux density for a given magnetization distribution can be estimated. With the magnetization as an additional unknown, inverse magnetostatic problems can be solved. Augmenting the magnetostatic energy with additional energy terms, micromagnetic problems can be solved. We demonstrate the use of physics informed neural networks for solving magnetostatic problems, computing the magnetization for inverse problems, and calculating the demagnetization curves for two-dimensional geometries.

cond-mat.mtrl-sci↗

arXiv2021

Conditional physics informed neural networks

We introduce conditional PINNs (physics informed neural networks) for estimating the solution of classes of eigenvalue problems. The concept of PINNs is expanded to learn not only the solution of one particular differential equation but the solutions to a class of problems. We demonstrate this idea by estimating the coercive field of permanent magnets which depends on the width and strength of local defects. When the neural network incorporates the physics of magnetization reversal, training can be achieved in an unsupervised way. There is no need to generate labeled training data. The presented test cases have been rigorously studied in the past. Thus, a detailed and easy comparison with analytical solutions is made. We show that a single deep neural network can learn the solution of partial differential equations for an entire class of problems.

cs.LG↗

arXiv2020

Microstructure Role in Permanent Magnet Eddy Current Losses

The impact of granular microstructure in permanent magnets on eddy current losses are investigated. A numerical homogenization procedure for electrical conductivity is defined. Then, an approximated simple analytical model for the homogenized conductivity able to capture the main features of the geometrical and material dependences is derived. Finally eddy current losses analytical calculations are given, and the two asymptotic expressions for losses in the stationary conduction limit and advanced skin effect limit are derived and discussed.

physics.app-ph↗

arXiv2019

Extracting local switching fields in permanent magnets using machine learning

Microstructural features play an important role for the quality of permanent magnets. The coercivity is greatly influenced by crystallographic defects, which is well known for MnAl-C, for example. In this work we show a direct link of microstructural features to the local coercivity of MnAl-C grains by machine learning. A large number of micromagnetic simulations is performed directly from Electron Backscatter Diffraction (EBSD) data using an automated meshing, modeling and simulation procedure. Decision trees are trained with the simulation results and predict local switching fields from new microscopic data within seconds.

physics.comp-ph↗

arXiv2019

Micromagnetics of rare-earth efficient permanent magnets

The development of permanent magnets containing less or no rare-earth elements is linked to profound knowledge of the coercivity mechanism. Prerequisites for a promising permanent magnet material are a high spontaneous magnetization and a sufficiently high magnetic anisotropy. In addition to the intrinsic magnetic properties the microstructure of the magnet plays a significant role in establishing coercivity. The influence of the microstructure on coercivity, remanence, and energy density product can be understood by {using} micromagnetic simulations. With advances in computer hardware and numerical methods, hysteresis curves of magnets can be computed quickly so that the simulations can readily provide guidance for the development of permanent magnets. The potential of rare-earth reduced and free permanent magnets is investigated using micromagnetic simulations. The results show excellent hard magnetic properties can be achieved in grain boundary engineered NdFeB, rare-earth magnets with a ThMn12 structure, Co-based nano-wires, and L10-FeNi provided that the magnet's microstructure is optimized.

cond-mat.mtrl-sci↗

arXiv2019

Learning magnetization dynamics

Deep neural networks are used to model the magnetization dynamics in magnetic thin film elements. The magnetic states of a thin film element can be represented in a low dimensional space. With convolutional autoencoders a compression ratio of 1024:1 was achieved. Time integration can be performed in the latent space with a second network which was trained by solutions of the Landau-Lifshitz-Gilbert equation. Thus the magnetic response to an external field can be computed quickly.

cond-mat.mtrl-sci↗

arXiv2018

Magnetic microstructure machine learning analysis

We use a machine learning approach to identify the importance of microstructure characteristics in causing magnetization reversal in ideally structured large-grained Nd$_2$Fe$_{14}$B permanent magnets. The embedded Stoner-Wohlfarth method is used as a reduced order model for determining local switching field maps which guide the data-driven learning procedure. The predictor model is a random forest classifier which we validate by comparing with full micromagnetic simulations in the case of small granular test structures. In the course of the machine learning microstructure analysis the most important features explaining magnetization reversal were found to be the misorientation and the position of the grain within the magnet. The lowest switching fields occur near the top and bottom edges of the magnet. While the dependence of the local switching field on the grain orientation is known from theory, the influence of the position of the grain on the local coercive field strength is less obvious. As a direct result of our findings of the machine learning analysis we show that edge hardening via Dy-diffusion leads to higher coercive fields.

cond-mat.mtrl-sci↗

arXiv2018

Tuning magnetocrystalline anisotropy of Fe$_{3}$Sn by alloying

The electronic structure, magnetic properties and phase formation of hexagonal ferromagnetic Fe$_{3}$Sn-based alloys have been studied from first principles and by experiment. The pristine Fe$_{3}$Sn compound is known to fulfill all the requirements for a good permanent magnet, except for the magnetocrystalline anisotropy energy (MAE). The latter is large, but planar, i.e. the easy magnetization axis is not along the hexagonal c direction, whereas a good permanent magnet requires the MAE to be uniaxial. Here we consider Fe$_{3}$Sn$_{0.75}$M$_{0.25}$, where M= Si, P, Ga, Ge, As, Se, In, Sb, Te and Bi, and show how different dopants on the Sn sublattice affect the MAE and can alter it from planar to uniaxial. The stability of the doped Fe$_{3}$Sn phases is elucidated theoretically via the calculations of their formation enthalpies. A micromagnetic model is developed in order to estimate the energy density product (BH)max and coercive field $μ_{0}$H$_{c}$ of a potential magnet made of Fe$_{3}$Sn$_{0.75}$Sb$_{0.25}$, the most promising candidate from theoretical studies. The phase stability and magnetic properties of the Fe$_{3}$Sn compound doped with Sb and Mn has been checked experimentally on the samples synthesised using the reactive crucible melting technique as well as by solid state reaction. The Fe$_{3}$Sn-Sb compound is found to be stable when alloyed with Mn. It is shown that even small structural changes, such as a change of the c/a ratio or volume, that can be induced by, e.g., alloying with Mn, can influence anisotropy and reverse it from planar to uniaxial and back.

cond-mat.mtrl-sci↗

arXiv2018

Preconditioned nonlinear conjugate gradient method for micromagnetic energy minimization

Fast computation of demagnetization curves is essential for the computational design of soft magnetic sensors or permanent magnet materials. We show that a sparse preconditioner for a nonlinear conjugate gradient energy minimizer can lead to a speed up by a factor of 3 and 7 for computing hysteresis in soft magnetic and hard magnetic materials, respectively. As a preconditioner an approximation of the Hessian of the Lagrangian is used, which only takes local field terms into account. Preconditioning requires a few additional sparse matrix vector multiplications per iteration of the nonlinear conjugate gradient method, which is used for minimizing the energy for a given external field. The time to solution for computing the demagnetization curve scales almost linearly with problem size.

physics.comp-ph↗

arXiv2017

Transition Jitter in Heat Assisted Magnetic Recording by Micromagnetic Simulation

In this paper we apply an extended Landau-Lifschitz equation, as introduced by Baňas et al. for the simulation of heat-assisted magnetic recording. This equation has similarities with the Landau-Lifshitz-Bloch equation. The Baňas equation is supposed to be used in a continuum setting with sub-grain discretization by the finite-element method. Thus, local geometric features and nonuniform magnetic states during switching are taken into account. We implement the Baňas model and test its capability for predicting the recording performance in a realistic recording scenario. By performing recording simulations on 100 media slabs with randomized granular structure and consecutive read back calculation, the write position shift and transition jitter for bit lengths of 10nm, 12nm, and 20nm are calculated.

physics.comp-ph↗

arXiv2017

Micromagnetic Simulations for Coercivity Improvement through Nano-Structuring of Rare-Earth Free L1$_0$-FeNi Magnets

In this work we investigate the potential of tetragonal L1$_0$ ordered FeNi as candidate phase for rare earth free permanent magnets taking into account anisotropy values from recently synthesized, partially ordered FeNi thin films. In particular, we estimate the maximum energy product ($BH$)$_\mathrm{max}$ of L1$_0$-FeNi nanostructures using micromagnetic simulations. The maximum energy product is limited due to the small coercive field of partially ordered L1$_0$-FeNi. Nano-structured magnets consisting of 128 equi-axed, platelet-like and columnar-shaped grains show a theoretical maximum energy product of 228 kJ/m$^3$, 208 kJ/m$^3$, 252 kJ/m$^3$, respectively.

physics.comp-ph↗

arXiv2016

Switching field distribution of exchange coupled ferri-/ferromagnetic composite bit patterned media

We investigate the switching field distribution and the resulting bit error rate of exchange coupled ferri-/ferromagnetic bilayer island arrays by micromagnetic simulations. Using islands with varying microstructure and anisotropic properties, the intrinsic switching field distribution is computed. The dipolar contribution to the switching field distribution is obtained separately by using a model of a triangular patterned island array resembling $1.4\,\mathrm{Tb/in}^2$ bit patterned media. Both contributions are computed for different thickness of the soft exchange coupled ferrimagnet and also for ferromagnetic single phase FePt islands. A bit patterned media with a bilayer structure of FeGd($5\,\mathrm{nm}$)/FePt($5\,\mathrm{nm}$) shows a bit error rate of $10^{-4}$ with a write field of $1.16\,\mathrm{T}$.

physics.comp-ph↗