France



With recent advancements in fabrication techniques, in particular through FEBID technology, three-dimensional (3D) nanomagnetism has emerged as a new field of interest [1]. Over the past decades, extensive studies on patterned two-dimensional thin-film elements have demonstrated that the properties of magnetic nanostructures are significantly affected by their shape, such that certain magnetic features can be generated and modified by changing the geometry. Following this principle, the access to an additional degree of freedom in 3D nanomagnetism can introduce new effects unknown in planar structures and may open up possibilities for new devices with artificially designed properties [2]. In this context, a promising category of novel nanomagnetic nano-architectures with remarkable potential for applications are 3D arrays of interconnected magnetic nanowires. The assembly of magnetic nanowires into an interconnected array can lead to interactions and magnetic structures with features known from artificial spin ice (ASI) lattices. A particular micromagnetic situation occurs at the intersections of the wires, where the magnetization must adapt to a change of both the geometry and the magnetization direction. At the vertices, characteristic magnetic configurations and phenomena of magnetic frustration unfold. Here we show that these frustrated states can strongly impact the overall properties of a bucky-ball type geometry, which can be considered a prototype of a 3D ASI.&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;Using finite-element micromagnetic simulations, we investigate the hysteretic behavior, the static magnetization structure, and the high-frequency properties of the magnetization in artificial buckyball-type nanostructures. The buckyball geometry [3, 4] acts as a model system to study the transition from two-dimensional to three-dimensional ASI structures. It has, on one hand, important similarities with a two-dimensional Kagomé lattice, while, on the other hand, it also incorporates distinct three-dimensional properties resulting from the spatial arrangement of the nanowires and the spherical shape of the network that they form. The geometry consists of 90 nanowires connected at 60 vertices, resulting in a closed network of hexagons and pentagons. Our model system is made of soft-magnetic Co, and its size is typically in the micron range. More specifically, we have investigated various buckyball-type geometries with individual nanowire lengths ranging between 25 nm and 250 nm.&lt;br/&gt;&lt;br/&gt;At zero field, the static magnetization structure of the magnetic bucky-balls is characterized by their configurations at the vertices. Due to the prevailing impact of shape anisotropy, each nanowire is magnetized along its axis. Based on the orientation of the magnetization in the three nanowires meeting at a vertex, the magnetic structures at the vertices can be classified into four types: two-out / one-in, two-in / one-out, three-out, and three-in. A specific magnetic charge, +1,-1,+3 and -3, can be assigned to each of these configurations, as is done in Kagomé lattices [5]. Neglecting the sign of the charges, one can distinguish between ice-rule obeying single-charge and defect-like triple-charge configurations. By means of hysteresis loop simulations, we find that triple-charged vertices can be created and removed through minor loops: If the field is reduced to zero from saturation, two triple-charge vertex configurations form at diametrically opposite sites. These triple charge defects dissolve after applying a moderate negative field, and they do not reappear if the field is subsequently removed again. Hence, we show that in these 3D ASI geometries, it is possible to inject and to remove magnetic triple-charge defect structures in a controlled way by varying the external magnetic field within a specific range.&lt;br/&gt;&lt;br/&gt;Going further in the study, we analyze the fundamental high-frequency modes of the magnetization in these geometries and their dependence on the magnetic structure. To extract the modes, the equilibrium states at zero field are excited by a picosecond field pulse, leading to a magnetic ringing over several nanoseconds. From these high-frequency dynamics, the magnetic modes are derived by using spatially-resolved windowed Fourier analysis [6]. We observe a distinct correlation between the distribution of magnetic charges at the vertices and the frequency of the modes. The modes can be distinguished as oscillations that are localized either at the vertices or within the nanowires. In particular, we find that the dynamics of the triple-charge states is very different from the dynamics at the single-charge vertices. The triple-charge states generate a strong, often dominating signal at low frequencies, which is absent in configurations without triple-charge defects. Moreover, we show that the frequency of this mode can be varied in an almost linear fashion by an external magnetic field. The combination of this feature with the particular hysteretic properties of the system opens a promising perspective to develop tailored devices with desired high-frequency properties that can be manipulated by varying the type of magnetic frustrations.

**References:**

[1] A. Fernández-Pacheco et al., Nat Commun 8, 15756 (2017)&lt;br/&gt;[2] P. Fischer et al., APL Materials 8, 010701 (2020)&lt;br/&gt;[3] L. Keller and M. Huth, Beilstein J. Nanotechnol. 9, 2581 (2018)&lt;br/&gt;[4] C. Donnelly et al., Phys. Rev. Lett. 114, 115501 (2015)&lt;br/&gt;[5] F. Montaigne et al., Sci. Rep. 4 : 5702 (2014)&lt;br/&gt;[6] M. Yan, R. Hertel, C. M. Schneider, Phys. Rev. B 76, 094407 (2007)

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/d48aacc7c01215a7aa016921fa518153.jpg)
Fig.1: Finite-element discretization of a 3D buckyball-type nanostructure. Depending on the buckyball size, we use up to 1.9 million elements.

![](https://assets.underline.io/uploads/markdown_image/1/image/7c6422b1b28117f73a2f02635cba7c87.jpg)
Fig. 2: a) Different zero-field configurations can be accessed through hysteresis loops and minor loops, in particular the triple-charge state and the single-charge state. b) Simulated Fourier spectrum of the magnetic modes of the triple-charge and single-charge state.

INTERMAG 2021

Micromagnetics of Frustrated States and High-frequency Modes in Artificial Buckyball Nanostructures

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With recent advancements in fabrication techniques, in particular through FEBID technology, three-dimensional (3D) nanomagnetism has emerged as a new field of interest [1]. Over the past decades, extensive studies on patterned two-dimensional thin-film elements have demonstrated that the properties of magnetic nanostructures are significantly affected by their shape, such that certain magnetic features can be generated and modified by changing the geometry. Following this principle, the access to an additional degree of freedom in 3D nanomagnetism can introduce new effects unknown in planar structures and may open up possibilities for new devices with artificially designed properties [2]. In this context, a promising category of novel nanomagnetic nano-architectures with remarkable potential for applications are 3D arrays of interconnected magnetic nanowires. The assembly of magnetic nanowires into an interconnected array can lead to interactions and magnetic structures with features known from artificial spin ice (ASI) lattices. A particular micromagnetic situation occurs at the intersections of the wires, where the magnetization must adapt to a change of both the geometry and the magnetization direction. At the vertices, characteristic magnetic configurations and phenomena of magnetic frustration unfold. Here we show that these frustrated states can strongly impact the overall properties of a bucky-ball type geometry, which can be considered a prototype of a 3D ASI. Using finite-element micromagnetic simulations, we investigate the hysteretic behavior, the static magnetization structure, and the high-frequency properties of the magnetization in artificial buckyball-type nanostructures. The buckyball geometry [3, 4] acts as a model system to study the transition from two-dimensional to three-dimensional ASI structures. It has, on one hand, important similarities with a two-dimensional Kagomé lattice, while, on the other hand, it also incorporates distinct three-dimensional properties resulting from the spatial arrangement of the nanowires and the spherical shape of the network that they form. The geometry consists of 90 nanowires connected at 60 vertices, resulting in a closed network of hexagons and pentagons. Our model system is made of soft-magnetic Co, and its size is typically in the micron range. More specifically, we have investigated various buckyball-type geometries with individual nanowire lengths ranging between 25 nm and 250 nm. At zero field, the static magnetization structure of the magnetic bucky-balls is characterized by their configurations at the vertices. Due to the prevailing impact of shape anisotropy, each nanowire is magnetized along its axis. Based on the orientation of the magnetization in the three nanowires meeting at a vertex, the magnetic structures at the vertices can be classified into four types: two-out / one-in, two-in / one-out, three-out, and three-in. A specific magnetic charge, +1,-1,+3 and -3, can be assigned to each of these configurations, as is done in Kagomé lattices [5]. Neglecting the sign of the charges, one can distinguish between ice-rule obeying single-charge and defect-like triple-charge configurations. By means of hysteresis loop simulations, we find that triple-charged vertices can be created and removed through minor loops: If the field is reduced to zero from saturation, two triple-charge vertex configurations form at diametrically opposite sites. These triple charge defects dissolve after applying a moderate negative field, and they do not reappear if the field is subsequently removed again. Hence, we show that in these 3D ASI geometries, it is possible to inject and to remove magnetic triple-charge defect structures in a controlled way by varying the external magnetic field within a specific range. Going further in the study, we analyze the fundamental high-frequency modes of the magnetization in these geometries and their dependence on the magnetic structure. To extract the modes, the equilibrium states at zero field are excited by a picosecond field pulse, leading to a magnetic ringing over several nanoseconds. From these high-frequency dynamics, the magnetic modes are derived by using spatially-resolved windowed Fourier analysis [6]. We observe a distinct correlation between the distribution of magnetic charges at the vertices and the frequency of the modes. The modes can be distinguished as oscillations that are localized either at the vertices or within the nanowires. In particular, we find that the dynamics of the triple-charge states is very different from the dynamics at the single-charge vertices. The triple-charge states generate a strong, often dominating signal at low frequencies, which is absent in configurations without triple-charge defects. Moreover, we show that the frequency of this mode can be varied in an almost linear fashion by an external magnetic field. The combination of this feature with the particular hysteretic properties of the system opens a promising perspective to develop tailored devices with desired high-frequency properties that can be manipulated by varying the type of magnetic frustrations.

**References:**

[1] A. Fernández-Pacheco et al., Nat Commun 8, 15756 (2017) [2] P. Fischer et al., APL Materials 8, 010701 (2020) [3] L. Keller and M. Huth, Beilstein J. Nanotechnol. 9, 2581 (2018) [4] C. Donnelly et al., Phys. Rev. Lett. 114, 115501 (2015) [5] F. Montaigne et al., Sci. Rep. 4 : 5702 (2014) [6] M. Yan, R. Hertel, C. M. Schneider, Phys. Rev. B 76, 094407 (2007)

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/d48aacc7c01215a7aa016921fa518153.jpg)
Fig.1: Finite-element discretization of a 3D buckyball-type nanostructure. Depending on the buckyball size, we use up to 1.9 million elements.

![](https://assets.underline.io/uploads/markdown_image/1/image/7c6422b1b28117f73a2f02635cba7c87.jpg)
Fig. 2: a) Different zero-field configurations can be accessed through hysteresis loops and minor loops, in particular the triple-charge state and the single-charge state. b) Simulated Fourier spectrum of the magnetic modes of the triple-charge and single-charge state.

technical paper

We use micromagnetic simulations based on the stochastic Landau-Lifshitz-Gilbert (sLLG) equation to calculate dynamic magnetic hysteresis loops relevant to magnetic nanoparticle hyperthermia. Our simulation model is inspired by a preclinical study reported by Dennis et al. [1] using iron oxide nanoparticles for cancer treatment in mice. The nanoparticles consist of nanorods nominally 6.7 nm x 20 nm x 47 nm in size. Instead of simulating atomic spins, micromagnetics suggests assuming uniformly magnetized cells with dimensions of the cubic unit cell (a0=0.839 nm), resulting in a rod composed of 10752 cells. The approximation that all spins within a cell are uniformly aligned via a relatively strong exchange interaction is valid at zero temperature for cell sizes smaller than the exchange length. At finite temperature, spin decorrelation must be taken into account, and a coarse-graining procedure is required to determine how micromagnetic parameters vary with cell size and temperature. To avoid unwanted heating of surrounding healthy tissues due to Eddy currents, restrictions on the applied field frequency and amplitude limit the field sweep rate to SR=4Hmax f &lt; 0.25 Oe/ns [2, 3]. Simulating a rod made of cells with size a0 takes months, so using fewer but larger cells is computationally attractive. Our study models the rods using larger cells (of length ab = b a0 for b &gt; 1) to be able to simulate complex nanoparticles in a reasonable time (see Fig. 1). As the cell size affects the system’s simulated properties, magnetic parameters need to be scaled to keep the model’s magnetic response, i.e., m-H hysteresis loops, invariant with the simulation cell size. Scaling also appears within the realm of phase transitions and critical phenomena, driven by thermodynamic considerations, resulting in the powerful renormalization group (RG) approach based on the correlation length. In the present study, we use the RG approach of Grinstein and Koch [4] to determine how the field H(b), anisotropy constant K(b), and exchange constant A(b) should scale with b (see Fig. 1) at finite temperature. We modify their approach to describe the scaling of the magnetization m(b) by introducing a phenomenological parameter δ that accounts for the nonlinear magnetization temperature dependence [5]. We also extend their approach to include magnetostatic interactions that were not considered in Grinstein and Koch’s work. The scaling algorithm produces nearly identical loops over several decades in the model grain volume, as shown in the top four curves of Fig. 2; for details, see Ref. [6]. Larger cell sizes also allow using a larger Δt when solving the equations of motion. Using the scaling approach decreases the rod’s simulation time from months to days. At the heart of the LLG equation is α, responsible for describing energy loss from the macroscopic motions of the local magnetization to microscopic thermal motion [7]. A larger α signifies a faster loss of energy and a shorter relaxation time required to align the magnetic moments to the field and results in a smaller hysteresis loop. Likewise, a slower SR is equivalent to a longer measurement time and consequently yields a smaller loop. Considering Sharrock's work [8], which relates the coercive field to the relaxation time and attempt frequency, we argue that, for the small α, so long as SR/α = constant, the coercive field should remain the same. In Fig. 2, we plot the m-H hysteresis loops for two sets of values for SR and α, such that SR/α =2.5, and interestingly even for large values of α, the loops overlap. Using this approach reduces the simulation time from days to hours, for details see Ref. [5]. To summarize, we show here that the RG approach of Grinstein and Koch yields a scaling of magnetostatic, exchange and anisotropy parameters that, with some modification for the rescaling of the resulting magnetization, yields finite temperature nanorod hysteresis loops that are, to approximately 10%, invariant with cell size. Scaling results hold even to the point where the nanorod is represented by a single magnetization vector that experiences only anisotropy and self-demagnetization. In addition to the computational speedup resulting from the use of fewer micromagnetic cells, the invariance of loops when SR/α is fixed provides another avenue for computational efficiency by allowing one to use a larger SR than the target value. This paves the way for efficient simulations of nanoparticles (composed of nanorods) and their response to AC fields [6], in support of experimental studies of hyperthermia.

**References:**

[1] C. L. Dennis, et al. Nanotechnology, 20(39), 395103 (2009). [2] S. Dutz, and R. Hergt, Int. J. Hyperth., 29(8), 790-800 (2013). [3] R. Hergt, and S. Dutz, J. Magn. Magn. Mater., 311(1), 187-192 (2007). [4] G. Grinstein, and R. H. Koch, Phys. Rev. Lett., 90(20), 207201 (2003). [5] R. Behbahani, M. L. Plumer and I. Saika-Voivod, J. Phys.: Condens. Matter, 32, 35LT01 6pp (2020). [6] R. Behbahani, M. L. Plumer and I. Saika-Voivod, arXiv:2010.08848 (2020). [7] T. L. Gilbert, IEEE Trans. Magn., 40(6), 3443-3449 (2004). [8] M. Sharrock, and J. McKinney, IEEE Trans. Magn., 17(6), 3020-3022 (1981).

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/53d389db281adb19e35bb9b508f104a5.jpg)
Figure 1. Coarse-grained modelling of a magnetite nanorod. The smallest micromagnetic cell models eight atomic spins within a cubic unit cell of length a0 = 0.839 nm with a single magnetic moment. Ultimately, the nanorod is modelled as a single block, corresponding to b = 22. Spherical magnetic nanoparticles studied in Ref. 2 are made up of magnetite nanorods.

![](https://assets.underline.io/uploads/markdown_image/1/image/6ef098f20be94e527108ac4581410602.jpg)
Figure 2: Hysteresis loop invariance. The top four loops show invariance with cell size when magnetic parameters are scaled according to a modified RG prescription in addition to the magnetostatic interactions scaling. The last two loops show the invariance of MH loops when the ratio SR/α is constant.

Coarse-graining in micromagnetic simulations of dynamic hysteresis loops

First Order Reversal Curve (FORC) diagrams have over 30 years of history as a tool to characterise ensembles of magnetic particles reaching from applications in geophysics to detect the prevalence of carriers of paleomagnetic signals [1] to the measurement of magnetic recording media properties [2]. Using FORC diagrams as a purely quantitative tool remains an open topic of active research (see e.g. [3]), but they have proven to be useful as semi-quantitative instrument to determine traces of magnetic phases in a material mixture [4]. Based on this present understanding we introduce the idea of using FORC diagrams to detect the crystalline structure of the cobalt binder phase in tungsten carbide (WC-Co), a hard metal used in a wide range of high-tech applications (e.g. drills or cutting inserts) [5]. The goal is to interpret FORC diagrams from experimental measurements by micromagnetic simulations as a source of “big data” for a fast characterisation of tungsten carbide binders. Our proposed method goes significantly beyond state-of-the art techniques used for characterisation of WC-Co in particular, but also makes a contribution to the interpretability of FORC diagrams in general by applying machine learning to the large amount of data points available through FORCs. Here, we present our first results towards such an automated interpretation of FORC diagrams, which requires a substantial amount of training data from micromagnetic simulations. Such simulations provide the opportunity to assemble mixtures of particles with a known distribution of various properties, which should serve as labelled training data. We use energy minimization [6] to compute the magnetization curves of Co particles with sizes of 100 nm and uniaxial anisotropy. A reversal curve is computed starting at each point Hr on the major loop where the magnetization M has changed by a defined amount. Depending on the alignment of the anisotropy axes with respect to the field H different numbers and configurations of domain wall nucleation-propagation-annihilation sequences occur during a full reversal loop, leading to a large variation in the number of generated FORCs. To mitigate the necessary computational efforts to acquire the training data of large ensembles of more than 100 particles we developed a Python-based FORC analysis framework and used scikit-learn’s Random Forest Regressor [7] to predict the surface M(H,Hr) from a subset of simulations. Our first results for an ensemble of non-interacting cubes with varying anisotropy axes show that the FORC diagrams calculated from the predicted M(H,Hr) surface give a valid representation of the FORC diagram calculated directly from the fully simulated ensemble (Fig.1). This approach will enable us to speed up the generation of training data considerably, which provides the basis to understand the microstructure of WC-Co samples containing Co particles with dimensions beyond the 100 nm scale by using FORCs as a fast and non-destructive characterisation tool. Fig. 2 shows two examples of simulated FORCs and their respective FORC diagrams. In Fig. 2a the external field is aligned more parallel to the particle’s easy axis than in Fig. 2b. As FORC diagrams only show pronounced peaks for irreversible processes, they can be directly associated with the jumps present in the FORCs shown in the upper part. Analytical models to explain the peaks’ positions and heights can only be found for simplified models. Following [8], the two peaks in Fig. 2a can be modelled analytically using a curvilinear hysteresis loop, but this model does not reveal the exact reversal process in the material. Combining micromagnetic simulations with machine learning will enable us to train models using realistic assumptions about the material of interest. As a first step the presented approach will make it possible to get data for the interpretation of FORC diagrams within minutes instead of weeks by replacing time-consuming micromagnetic simulations with sufficiently trained machine learning models. The financial support by the Austrian Federal Ministry of Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK) in the KI-Carbide project (#877141) is gratefully acknowledged.

**References:**

[1] A. R. Muxworthy and D. J. Dunlop: Earth and Planet. Sci. Lett. 203, pp 369-382 (2002) [2] M. Winklhofer and G. T. Zimanyi, J. Appl. Phys. 99, 08E710 (2006) [3] D. A. Gilbert, G.T. Zimanyi, R.K. Dumas et al., Sci. Rep. 4, 4204 (2014) [4] C. Carvallo et al.: Phys. Earth Planet. Inter. 154, pp 308–322 (2006) [5] J. García et al., Int. J. Refr. Met. 80, pp 40-68 (2019) [6] L. Exl, J. Fischbacher, A. Kovacs et al., Comp. Phys. Comm. 235, pp 179-186 (2019) [7] F. Pedregosa et al, JMLR 12, pp 2825-2830 (2011) [8] C. Pike and A. Fernandez, J. Appl. Phys. 85, pp 6668-6676 (1999)

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/9de8549ddbc61514a201eecc9b7f0b4d.jpg)
Fig. 1 FORC diagrams computed (a) directly from simulating a full ensemble of 125 non-interacting hcp-Co particles with varying alignment of the anisotropy axis with respect to the external field and (b) by using only a subset of 20 simulations as input to a Random Forest Regressor to predict the M(H, Hr) surface

![](https://assets.underline.io/uploads/markdown_image/1/image/2c361c16a150eeb7f7d6915c3daf9b09.png)
Fig. 2 Examples of simulated FORCs and calculated FORC diagrams for a 100 nm cube of hcp Co (uniaxial anisotropy). Colorbars indicate the peak height of the FORC distribution (see also Figure 1). The external field in (a) is aligned more parallel to the easy axis than in (b). The visible peaks in the FORC diagrams (bottom) correspond to irreversible jumps of the magnetization (top).

Using a Random Forest Regressor to Predict First-Order Reversal Curves of hcp-Co Particle Ensembles

The Stoner-Wohlfarth model [1], first published in 1948, describes the magnetic behaviour of anisotropic, ferromagnetic particles, which magnetize via coherent rotation. This widely used model predicts the crossing of the ascending and descending branches of the magnetization curve when the applied field is close to the magnetic hard-axis. However, experimental observation of this hysteresis branch crossing has not been acknowledged in the literature. In fact, the crossing of hysteresis branches has been labelled as being nonphysical [2] and a problem [3] with the Stoner-Wohlfarth model. Here we show that the crossing of the ascending and descending branches of the magnetization curve does occur and is consistent with the Stoner-Wohlfarth model. Contrary to previous reports, we show that the crossing of hysteresis branches is physically realistic [4] and violates no fundamental law.

**References:**

[1] Stoner, E.C. and Wohlfarth, E.P., Phil. Trans. Roy. Soc. of London Series A, 240, no. 826, pp.599-624 (1948). [2] Vajda, F. and Della Torre, E., IEEE Trans. Mag., 28, no. 5, pp.2611-2613 (1992). [3] Tannous, C. and Gieraltowski, J., Physica B, 403, pp.3563-3570 (2008). [4] Heslop, D. and Roberts, A., Jour. Goephys. Res., 117, B03103 (2012).

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/960e20c09fe918c9d9087883ca10b05a.jpg)
Figure 1: The reduced x- and y-components of the magnetization as a function of applied field for an angle of α=2o. Circles and triangles represent the x- and y-components of the measured magnetization, respectively. Solid and dashed lines represent numeric solutions to a temperature-dependant Stoner-Wohlfarth model. Ascending and descending data are shown in red and blue, respectivey. Black circles highlight the hysteresis branch crossings.

Hysteresis branch crossing and the Stoner-Wohlfarth model

I. Introduction The iron losses, permeability and magnetostriction of electrical steel sheet are sensitive to mechanical stress, and the presence of mechanical stress may bring out the deformation of the magnetostrictive hysteresis loops, the increase of core loss, and the aggravation of vibration and noise in electrical machines or transformers. The accurate prediction of magnetic characteristics under stress is significant for optimum design of high power density motor. The multi-scale domain energy models [1-2], such as the assembled domain structure model (ADSM) have been conducted to represent the magnetic hysteretic behavior in an electrical steel sheet based on the minimization principle of total magnetic energy. In comparison with some phenomenon models such as Preisach, the domain energy model interprets the magnetic domain wall movements and rotation with the magnetic field applied and builds a relevance between the microstructure and magnetic hysteresis properties. However, due to the complexity of the parameter identification, the establishment of domain model is at the cost of large computation time and its modeling accuracy to the magnetostrictive loops is also to be improved. In this paper, a multi-scale domain energy model is improved to estimate the magnetostrictive characteristics under different external stresses, in which the hysteresis energy function is introduced instead of pinning energy in order to reduce the computation time. To identify the parameters in the model, the magnetic domain wall movements in an electrical steel sheet during the magnetization process are observed by a magnetic domain observation microscope, and the magnetostrictive hysteresis loops under several external stresses are measured. The validity of proposed model is verified by comparing the measured magnetostrictive loops with computed ones. II. Experiments and Approaches A. Experiments With the presence of external magnetic field intensity H, the balance state with the total inner energy in the ferromagnetic materials keeping the minimum is disturbed, and then the wall movements and magnetic moment rotation in the magnetic domains is occurring. This magnetization process in an electrical steel sheet is observed with a polished sample of with the size of 8mm width and 8mm length, as shown in Fig.1(a), by means of a magnetic domain observation microscope, BH-782PI-SHG, Japan. Fig.1(b) represents a domain figure under a 0.5Oe field in a non-oriented silicon steel, in which the difference between grey levels indicates different magnetization direction domains. Further, the magnetic hysteresis and magnetostrictive loops with different mechanical stresses applied are measured, as shown in Fig.1(b), by a laser magnetostrictive tester from Brockhaus, Germany, in which the shape and magnitude and area of magnetostrictive loops occur to increase after applying a 4Mpa compressive stress. B. Improved Multi-Scale Domain Energy Model The magnetic domain moment is alignment with the easy directions of the crystal in a grain when the material is in the demagnetized state. According to the ADSM model each grain is assumed to be composed of six magnetic domains along the easy directions, and the unit magnetization vector m of each domain is denoted by the variables θ and Φ as a vector [sinθsinΦ, sinθcosΦ, cosθ]. The θi ,Φi and the existence probability ri ( i=1,2,…,6) of magnetization vector for each domain can be determined according to the principle of local energy minimum. The total magnetic domain energy is expressed as the summation of Zeeman energy, the crystalline anisotropic energy, the magnetostatic energy and magnetoelastic energy in the ADSM. Further in order to model the magnetic hysteresis effect, the pinning energy have been added into the total energy. In this paper, in order to estimate the behavior of magnetostrictive hysteresis behavior, a hysteresis energy density function in (1) instead of the pinning energy is employed into the total domain energy in terms of simplifying the parameter identification process. Eh=-μ0Ms/χ0(α1,imx-p+α2,i+my-p+α3,imz-p) (1) where [α1,i, α2,i, α3,i] is direction cosine of the unit magnetization vector of the i-th domain with respect to the three easy axes; (mx-p, my-p, mz-p) is three components of magnetization vector at previous moment, MS is the saturation magnetization. The hysteresis energy density function can take the effect of the magnetization history, and explain the hysteresis phenomenon as an inertia. Further, the effect of external stress on the magnetostriction in the magnetoelastic energy is described as: Eσ=-3/2λ100σ(α21,iγ21+α22,iγ23+α23,iγ23)-3λ111σ(α1,iα2,iγ1γ2+α2,iα3,iγ2γ3+α3,iα1,iγ3γ1) (2) where (γ1, γ2, γ3) is the cosine of the angle between the stress and the three easy axes; λ100 and λ111 are saturation magnetostrictive coefficients along the [100] direction and the [111] direction; σ is the stress applied on the sample; Fig. 2 shows the measured and modeled results of magnetostriction loops, and more discussion will be presented in detail in the extended paper.

**References:**

 [1] S. Ito, T. Mifune, T. Matsuo, et al. Energy-Based Magnetization and Magnetostriction Modeling of Grain-Oriented Silicon Steel under Vectorial Excitations . IEEE Trans. Magn. vol 52, no. 5, pp. 1-4, 2016. [2] D. Vanoost，S. Steentjes, J. Peuteman, et al. Magnetic hysteresis at the domain scale of a multi-scale material model for magneto-elastic behaviour. J. Magn. Magn. Mater. vol 414, no. sep., pp. 168-179, 2016. [3] T. Moses. Relevance of microstructure and texture to the accuracy and interpretation of 1 and 2 directional characterisation and testing of grain-oriented electrical steels. International Journal of Applied Electromagnetics & Mechanics, vol. 55, pp. 1-11, 2017.

**Images:**
![](https://assets.underline.io/uploads/markdown_image/1/image/fd54ab2a2bbd0678fa4e3b7e947e9359.jpg)

![](https://assets.underline.io/uploads/markdown_image/1/image/7ac2dc401cdea94bc7da44b75d6694f5.jpg)

Estimation of Magnetostrictive Hysteresis Properties of Electrical Steel Sheet Under External Stress Using Multi-Scale Domain Energy Model

Magnetostriction is one of main factors causing the core vibration and noise of electromagnetic equipment, and the magnetostriction at the stator core and T-joint in transformers has the vector characteristics due to the two-dimensional (2D) rotational magnetization. Recently, the 2D vector magnetostriction models[1,2] have been researched based on the Fourier analysis of measured 2D magnetostrictive waveforms and magnetic flux density ones. But, owing to the complexity of rotational magnetization, it is more significant to describe the magnetostrictive characteristics in terms of magnetization mechanism. In this paper, with the reference to the hysteresis behavior and irreversibility in the Jiles-Atherton model, the hysteresis characteristics between the magnetostiction λ and the magnetization M are attempted to be explained by the pinning effect. After that, a 2D vector magnetostriction model is proposed and its validity is verified with experimental data from a 2D rotational magnetization tester. I. The Proposed 2D Vector Magnetostriction Model The magnetostriction λ and magnetization M are usually expressed as a square function relationship when they are static and do not consider saturation， λ=pM2 (1) A large number of experimental observations show that the change of magnetostriction λ lags behind the variation of magnetization M within one magnetization time period. For the ferromagnetic material, with the magnetic field applied, the domain wall displacement may be hindered by the pinning effect, which results from the impurity and cavity in the material, so the quantitative hysteresis behavior and irreversibility are mainly caused by overcoming the pinning. As a result, the hysteresis behavior of magnetostriction can be also explained by the pinning effect, in which the magnetostriction is mainly due to the movement of domain walls whose spontaneous magnetization is not alignment with the applied magnetic field. By adding the irreversibility of magnetostriction into (1) and taking the vector properties of 2D magnetization into account, a 2D vector magnetostriction model is proposed as λxx=pλxx[cλxxManx+(1-cλxx)Mirrλx]2/Msx2+pλyy[cλyyMany+(1-cλyy)Mirrλy]2/Msy2 λxx=qλxx[cλxxManx+(1-cλxx)Mirrλx]2/Msx2+qλyy[cλyyMany+(1-cλyy)Mirrλy]2/Msy2 (2) γxy=rγxy[cλxxManx+(1-cλxx)Mirrλx][cλyyMany+(1-cλyy)Mirrλy]/(MsxMsy) where λxx is the normal strain in x direction, λyy is the normal strain in y direction, and γxy is the shear strain; Msx and Msy are two components of saturation magnetization; Manx and Many are two components of ideal anhysteretic magnetization in the x,y direction; the parameters of cλxx, cλyy, pλxx, pλyy, qλxx, qλyy, and rrxy are to be determined from the experimental data of 2D magnetic properties; Mirrλx and Mirrλy denote the irreversible component of the magnetization and can be described as Mirrλ=Man-kλδ(dMirrλ/dHe) (3) where kλ is the pinning constant and obtained through experimental data; δ=(dHe/dt)/|dHe/dt|; He is the effective field . II. Experiments and Model Verification A 2D magnetic properties tester with a rosette strain gauge detecting the magnetostrictive strain signals in [3] is used to provide the data with identifying the parameters in (2) and (3). Fig. 1 shows the measured magnetic flux density B loci and magnetic field intensity H trajectories. Fig. 2 shows the comparison of the calculated and measured values of magnetostriction at three time moments with respect to a B trajectory with the axis ratio α=0.2, Bmax=0.9T, and the inclination angle θB=45°.

**References:**

[1] Yamagashira M，Wakabayashi D，Enokizono M．”Vector magnetic properties and 2-D magnetostriction of various electrical steel sheets under rotating flux condition,” IEEE Trans. Magn., vol. 50, no. 4, pp. 1–4, Apr. 2014, Art. no. 6100404. [2] L. Zhu, H.-S. Yoon, H.-J. Cho, D.-J. Um, and C.-S. Koh, “Finite-element analysis of magnetostriction force in power transformer based on the measurement of anisotropic magnetostriction of highly grain-oriented electrical steel sheet,” IEEE Trans. Magn., vol. 52, no. 3, pp. 1–4, Mar. 2016, Art. no. 6100304. [3] Z. Wang, Y. Zhang, D. Zhang, D. Xie, C.-S. Koh, and O. A. “Modelling of magnetostrictive property of electrical steel sheet under vectorial excitation,” IEEE Trans. Magn., vol. 55, no. 6, pp. 1–4,Jun. 2019, Art. no. 7300304.

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A 2D Vector Magnetostriction Model in an Electrical Steel Sheet Taking the Pining Hysteresis Effect into account

The treatment of hysteresis models in the numerical solution of Maxwell equations represents major issues as large computational times and significant memory space allocations are required [1][2]. Stability issues of the iterative solution are also a non-negligible source of concern. The Jiles-Atherton (J-A) [3] and the Preisach models [4] are the most used and commented hysteresis models. On the one hand, memory management in the Jiles-Atherton model is simple but convergence often goes through large temporal discretization. On the other hand, Preisach model can work with a limited time discretization but it requires huge memory space and complex memory management. In [5][6], authors proposed an interesting way to take into account hysteresis solving both the J-A and the Preisach models issues. Hysteresis is taken into account through the following hypothesis: the slope dB/dH (reciprocally dH/dB) inside and over the major hysteresis loop envelope is supposed to be dependent on only three parameters: the magnetic excitation field H, the magnetic induction field B, the sign of the time derivation of the model input (B or H). Based on this simple assumption, a new way to take into account the quasi-static scalar hysteresis for ferromagnetic materials was described. This new method also called DSHM for “Derivative Static Hysteresis Model” is an elegant alternative technique, it can be implemented with a simple memory management and reduced memory allocation demands in respect to more classical approaches. Convergence and precise simulation results can be reached with a relatively coarse temporal discretization. This hysteresis taken into account is also easily reversible (from B(H) to H(B)) and easily exportable. Fig. 1 – Illustration of the DSHM matrix. Up to now, the filling step of the DSHM matrix (Fig. 1) has always been through a set of experimental first-order reversal curves (as illustrated in [5]). It is evident that getting such experimental results can be complicated in particular situations, including electromechanical converters design or electromagnetic nondestructive testing but in this study we propose to fill the DSHM model’s matrix with a reduced number of experimental data thanks to assumptions also used to parameter the Preisach and the Jiles-Atherton models. The principles of the new method can be detailed as follows: _ The Preisach or the J-A models are implemented first. It implies to determine an adequate combination of parameters for the J-A model or an adequate triangle density for the Preisach model. Such implementation can be done with a limited number of experimental results (a single major hysteresis cycle in the extreme case). _ Reversal curves are plotted numerically using the classic hysteresis models. _ The numerical reversal curves are used to fill the DSHM matrix. The DSHM model provides correct simulation results even under unsymmetrical waveform situations. Implemented in a nonlinear dynamic finite differences numerical scheme for the resolution of the magnetic field diffusion through the cross section of a magnetic lamination [7][8], it allows considerable speed-up of the simulation process while conserving very reasonable level of accuracy. To summarize: the hysteresis law is considered in a generalized input vector space, in which an interpolation matrix is constructed with the columns and rows denoting respectively the discrete values of H and B and whose values stand for the dB/dH (reciprocally dH/dB) slope at the corresponding points. In the end of this study, the resolution of the non-linear magnetic field diffusion through a ferromagnetic lamination is chosen as a benchmark simulation. It allows to compare the performances of space discretized based numerical solvers and to confirm the improved efficiency of the proposed method versus the J-A and the Preisach ones.

**References:**

[1] P. Zhou & al., IEEE Trans. On Mag. Dev., vol. 40, Iss. 4, pp. 1803 – 1809, (2004) [2] B. Ducharne & al., J. Phys. D: Appl. Phys., Vol. 40, pp. 551-555, (2007) [3] D.C. Jiles & al., J. App. Phys. 55, pp. 2115, (1984) [4] Y. Bernard & al., vol. 25, Iss. 1-4, pp. 729-733, (2007) [5] R. Scorretti & al., proceedings of compumag 2011, 8. Mat. modelling, 11. Num. techniques, (2011) [6] T-P Do & al. & al., proceedings of compumag 2009, (2009) [7] B. Gupta & al., IEEE Trans. on. Mag, Vol. 54, Iss. 3, (2018) [8] M. A. Raulet & al., IEEE Trans. on Mag., vol. 40, n° 2, pp. 872 – 875, (2004)

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Optimized magnetic hysteresis management for electromagnetic space discretization simulation tool.

Ferromagnetic materials are used in a wide range of electromagnetic applications (energy converters, sensors, inductances …). Ferromagnetic materials are characterized by a strong answer under the influence of a magnetic field [1]. These properties are of huge advantages in many applications but they are also strongly non-linear and frequency dependent. In this domain, we observe a growing interest in the development of simulation tools reducing the experimental campaigns and improving the knowledge and the performances. Accurate simulation results can only be obtained by coupling precise electromagnetic equations to the exact material laws (hysteresis, saturation, frequency dependence). Under the influence of an external magnetic excitation, the local magnetic state through the ferromagnetic specimen is ruled by the combination of both the magnetic domain kinetics and the external magnetic field diffusion. Fig. 1, Illustration of the ferromagnetic hysteresis frequency dependence. The usual methods for the simulation of the magnetic behavior are all based on the separation of the magnetic contributions, where the microscopic Eddy currents due to the domain wall motions and the macroscopic ones due to the extern magnetic field variations are considered separately [2]. This separation remains artificial, since practically both losses mechanisms occurs simultaneously and interact on each other. Alternative solutions for the simulation of these phenomena have already been proposed through the resolution of an anomalous fractional magnetic field diffusion (1, 2 or 3D depending on the experimental situation) [3][4]. The fractional order constitutes an additional degree of freedom in the simulation scheme. It is identified through comparisons to experimental results. By adjusting precisely this order, very accurate local and global simulation results can be obtained on a very broad frequency bandwidth [5]. It allows to predict precisely the dynamic magnetic behavior of classic ferromagnetic components. Fractional diffusion equation is an interesting method but this is not the only way to take into account the hysteresis frequency dependence using fractional derivative operators. Lump models based on time fractional differential equations is another option [6][7]. In both methods (the fractional diffusion equation and the time fractional differential equation), a numerical resolution of the Riemann-Liouville definition is required. Recent works have demonstrated the effectiveness of using quadrature techniques to approximate the Riemann–Liouville definition for fractional derivatives in the context of nonlinear viscoelastic model [8]. In this manuscript, the quadrature methods are implemented and tested in the case of the ferromagnetic hysteresis. Theoretical results are compared to experimental ones, and conclusions and perspectives are drawn such as possible improvements.

**References:**

[1] R. M. Bozorth, Wiley-IEEE Press, Michigan University, (1993) [2] G. Bertotti, IEEE Trans. Magn., vol. 24, iss. 1, pp. 621-630, (1988) [3] B. Ducharne & al., Com. in Nonlin. Sci. and Num. Sim., vol. 92, 105450, (2020) [4] B. Ducharne & al., The Eur. Phys. J. Plus, 135:325, (2020) [5] B. Ducharne & al., under revision, AIP advances, (2021) [6] B. Zhang & al., IEEE Trans. Magn., Iss. 99, pp. 1-5, (2018) [7] B. Zhang & al., IEEE Trans. Magn., Vol. 54 iss. 3, (2017) [8] P.R. Miles & al., J. Int. Mat. Syst and Struct., (2020)

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Ferromagnetic hysteresis model using fractional derivative resolution developed for the simulation of viscoelastic phenomena.

The discovery of a significant interfacial Dzyaloshinskii-Moriya Interaction (iDMI) in multi-layer thin films launched a fervent research effort into its impact on the behavior of chiral magnetic objects including domain walls (DWs).[1] The field driven creep of these Dzyaloshinskii domain walls as observed by Kerr microscopy remains a popular tool for evaluating the strength of the iDMI, but results are notoriously challenging to interpret. In this talk, we focus on experimental and theoretical investigations into the asymmetric field-driven expansion of magnetic domains in thin films with iDMI, which provide a fascinating landscape to understand creep of 1D elastic manifolds with an internal order parameter.[2] We will review early models of creep based on the DW energy, which matched well with certain experimental observations – namely, that an in-plane field (Hx) coupled with a perpendicular driving field (Hz) leads to asymmetric expansion of the domain as well as minima in the plot of x-velocity vs Hx. However, other common observations were not explained by this model including that, at large values of Hx, the high energy wall orientations reached or exceeded the velocity of low energy orientations. To explain this, Pellegren at el considered the more general elastic energy of the DW, given by the stiffness [σ(θ) + σ''(θ)].[3] By introducing a dispersive stiffness, which accounts for the finite lengthscale over which the wall deforms, it was indeed shown that the elastic energy of opposite extrema on the domain converge as Hx → ∞. This more general model explains some of the features that have also been explained by an attempt frequency that depends on wall chirality (via a chiral damping parameter).[4] The possible role of chiral damping will be discussed briefly. In more recent experiments, Brock et al showed that, in some cases – especially for weak DMI, the domain adopts a highly preferred growth direction with a dendritic structure that is neither parallel or anti-parallel to Hx.[5] The results cannot be explained by a chiral Bloch component to the DW, which would violate the symmetry of the system. Instead, we identify a steady-state DW substructure that results from the z-field torque on the internal DW magnetization (see figure 1c). This breaks the energetic mirror symmetry along the x-axis and leads to orientations of the DW with vanishing elastic energy (i.e. rapidly increased creep velocity). Remarkable agreement between an augmented form of the dispersive stiffness model (now accounting for steady-state configurations) and the experimental measurements is found. The material parameters that give rise to this seemingly anomalous behavior will be discussed. Other important factors such as internal DW excitations, field-dependent pinning, and a field-dependent DW width will be considered in this presentation as well.

**References:**

[1] Thiaville, A., Rohart, S., Jué, É., Cros, V. and Fert, A., 2012. Dynamics of Dzyaloshinskii domain walls in ultrathin magnetic films. EPL (Europhysics Letters), 100(5), p.57002. [2] Fournier, J. B. "Generalized gibbs-thomson equation and surface stiffness for materials with an orientational order parameter." Physical review letters 75.5 (1995): 854. [3] Pellegren, J.P., Lau, D. and Sokalski, V., 2017. Dispersive stiffness of Dzyaloshinskii domain walls. Physical Review Letters, 119(2), p.027203. [4] Jué, E., Safeer, C.K., Drouard, M., Lopez, A., Balint, P., Buda-Prejbeanu, L., Boulle, O., Auffret, S., Schuhl, A., Manchon, A. and Miron, I.M., 2016. Chiral damping of magnetic domain walls. Nature materials, 15(3), pp.272-277. [5] Brock, J., Medapalli, R. and Fullerton, E., 2020. Chirality-driven growth of stripe domains in Co/Ni/Pt-based multilayers. Bulletin of the American Physical Society, 65.

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/d041ea67aa2fad976e5c8d2fe441cb85.jpg)
Fig 1. Schematic of a Néel bubble subject to external magnetic field. Arrows indicate the direction of internal magnetization along the circumference. Green lines indicate time-reversal mirror planes, m'. a) No external field. Magnetic point symmetry: ∞m'm’. b) Hx only. Magnetic point symmetry: m’. c) Hx and Hz field. Magnetic point symmetry: 1.

Dzyaloshinskii Domain Wall Creep INVITED

Despite numerous recent studies focusing on the dynamics of pinned chiral magnetic textures [1-6], their interactions with weak pinning disorder is far from being understood. Basic issues such as the length-scale and the strength of interaction between domain walls (DWs) and defects in magnetic materials remain open. Our works [7] evidence a strong correlation between controlled variations of DW width and the characteristic length of pinning. The DW-disorder interaction strength is also shown to depend on DW energy and width. These findings should be also relevant for a wide variety of elastic interfaces moving in weak pinning disordered media. We explore the magnetic field-driven motion of domain walls with different chiralities in thin ferromagnetic films made of Pt/Co/Pt, Au/Co/Pt, and Pt/Co/Au. The displacement of DWs was observed by polar Kerr microscopy. An in-plane magnetic field Hx was used to control the magnetization direction in the DW, as well as its width. A pulse out-of-plane field H was used to move DW. The measured velocity corresponds to the ratio between the measured DW displacement and the pulse duration. The velocity curves obtained for Pt/Co/Au with different values of Hx are reported in Fig. 1. Their analysis with the self-consistent description of the universal creep and depinning regimes [8] was used to extract parameters (depinning field Hd and temperature Td) characterizing the interaction between domain wall and weak random pinning disorder of the films. Surprisingly, the values of Hd and Td vary (not-shown) with the in-plane magnetic field Hx, which strongly suggests that the pinning properties of DWs depend on their magnetic texture, in contrast to the usual assumption found in the literature [1-6]. To predict accurately the variation of DW energy and structure with in-plane and DMI fields, we have calculated the orientation of the magnetization M (x) for a DW plane perpendicular to Hx, from numerical micromagnetic calculations (MuMax3), using the micromagnetic parameters of the three samples (not-shown). For the DW width, we both use the geometrical Hubert and dynamic Thiele definitions. Upon varying Hx away from the magnetic field H_DMI associated to the Dzyaloshinskii-Moriya interaction (DMI), the magnetization within the DW progressively switches from Bloch to Néel configuration, and the DW width increases due to the Zeeman contribution, as expected. The DW surface energy decreases as Hx departs fromH_DMI, in qualitative agreement with the predictions of simplified models [3-5]. In order to discuss the DW pinning at microscopic scale as a function of in-plane field Hx, we use standard scaling arguments [8] based on the variation of the energy produced by the deformation of a DW. The DW energy is assumed to be controlled by the gain of Zeeman energy due to magnetization reversal, the elastic energy produced by the increase of DW length, and the pinning energy due to the fluctuations of DW energy produced by the weak disorder. From scaling arguments, we obtain the relations which link the characteristic range and force of the interaction between DW and pinning defects, to the measured depinning field Hd(Hx), temperature Td(Hx), and the predicted DW surface energy. For a comparison (see Fig. 2) with the predicted variations of the DW width, the values of the pinning range were rescaled with a constant factor, which is the only adjustable parameter. As it can be observed, there is a strong correlation between the variations of the pinning range and the DW width. This strong correlation suggests that the variations of domain wall structure, controlled by an in-plane field, modify the characteristic lengthscale of pinning, whatever the DW chirality and the interaction strength between domain wall and pinning defects.

**References:**

[1] S.-G. Je, D.-H. Kim, et al., Phys. Rev. B 88, 214401 (2013). [2] R. Lavrijsen, D. M. F. Hartmann, et al., Phys. Rev. B 91, 104414 (2015). [3] D. Lau, V. Sundar, et al., Phys. Rev. B 94, 060401 (2016). [4] J. P. Pellegren, D. Lau, and V. Sokalski, Phys. Rev. Lett. 119, 027203 (2017). [5] D. Lau, J. P. Pellegren, et al., Phys. Rev. B 98, 184410 (2018). [6] D. M. F. Hartmann, R. A. Duine, et al. , Phys. Rev. B 100, 094417 (2019). [7] P. Géhanne, S. Rohart, A. Thiaville, and V. Jeudy, Phys. Rev. Res. 2, 043134 (2020). [8] V. Jeudy, R. Diaz Pardo, et al., Phys. Rev. B 98, 054406 (2018).

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/4cb670d00ac41c65fa6b4dd07aff8bed.jpg)
Domain wall velocity versus out-of-plane magnetic field measured for a Pt/Co/Au film and different values of the in-plane field. The solid and dash lines are predictions for the creep and depinning regimes. The stars correspond to the coordinates of the depinning thresholds Hd(Hx).

![](https://assets.underline.io/uploads/markdown_image/1/image/1ff28aae80963ce5024f1005ff81dcb7.jpg)
Domain wall widths (Hubert: solid line) and (Thiele: dash line), and correlation length of the pinning versus in-plane magnetic field for the three films. For each film, the correlation length was rescaled to highlight its strong correlation with the DW widths.

Interaction of chiral domain walls with pinning disorder in thin ferromagnetic films

I will present an overview of our recent results on modelling field-driven dynamics of domain walls in ferromagnetic thin films and strips with perpendicular magnetic anisotropy, focusing on the effect of internal degrees of freedom on the domain wall dynamics. In the geometries considered, the dynamics associated with the internal degrees of freedom are given by that of the magnetisation at the domain wall mid-point, and is manifested as repeated nucleation, propagation and annihilation of topological defects known as Bloch lines within the moving domain wall. Our aim is to understand the effect of such internal dynamics on the large-scale dynamics of domain walls. First, I will discuss micromagnetic simulations of magnetic field-driven domain wall dynamics in thin ferromagnetic strips with perpendicular magnetic anisotropy which demonstrate that in wide enough strips the Walker breakdown - the sudden drop of domain wall velocity as the field is increased above the Walker field - is a multistep process: It consists of several distinct velocity drops separated by short linear parts of the velocity vs field curve. These features originate from the repeated nucleation, propagation, and annihilation of an increasing number of Bloch lines within the domain wall as the driving field magnitude is increased [1]. Second, I will consider micromagnetic simulations of Barkhausen noise in disordered Pt/Co/Pt thin films due to precessional motion of domain walls. In this regime the domain walls contain Bloch lines which repeatedly nucleate, propagate, and annihilate within the domain wall during the Barkhausen jumps. In addition to bursts of domain wall propagation, the in-plane Bloch line dynamics within the domain wall exhibits crackling noise and constitutes the majority of the overall spin rotation activity [2]. Finally, I will present our very recent work on developing a reduced model of line-like domain walls in thin films with perpendicular magnetic anisotropy including a description of internal degrees of freedom, i.e., Bloch lines and their dynamics. The model, which is computationally much more efficient than full micromagnetic simulations, is applied to study the depinning transition of field-driven domain walls in disordered ferromagnetic thin films [3].

**References:**

[1] J. Hütner, T. Herranen, and L. Laurson, Phys. Rev. B 99, 174427 (2019). [2] T. Herranen and L. Laurson, Phys. Rev. Lett. 122, 117205 (2019). [3] A. Skaugen and L. Laurson, in preparation (2020).

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