Modelling compensated antiferromagnetic interfaces with MuMax3

We show how compensated antiferromagnetic spins can be implemented in the micromagnetic simulation program MuMax3. We demonstrate that we can model spin flop coupling as a uniaxial anisotropy for small canting angles and how we can take into account the exact energy terms for strong coupling between a ferromagnet and compensated antiferromagnet. We also investigate the training effect in biaxial antiferromagnets and reproduce the training effect in a polycrystalline IrMn/CoFe bilayer.Comment: 11 pages + Supplementary Material (10 pages

Tomorrow’s micromagnetic simulations

Micromagnetic simulations are a valuable tool to increase our understanding of nanomagnetic systems and to guide experiments through parameter spaces that would otherwise be difficult and expensive to navigate. To fulfill this task, simulations have always pushed the limits of what is possible in terms of software and hardware. In this perspective, we give an overview of the current state of the art in micromagnetic simulations of ferromagnetic materials followed by our opinion of what tomorrow's simulations will look like. Recently, the focus has shifted away from exclusively trying to achieve faster simulations, toward extending pure micromagnetic calculations to a multiphysics approach. We present an analysis of how the performance of the simulations is affected by the simulation details and hardware specifications (specific to the graphics processing unit-accelerated micromagnetic software package mumax3), which sheds light on how micromagnetic simulations can maximally exploit the available computational power. Finally, we discuss how micromagnetic simulations can benefit from new hardware paradigms like graphics cards aimed at machine learning

Simultaneous coercivity and size determination of magnetic nanoparticles

Magnetic nanoparticles are increasingly employed in biomedical applications such as disease detection and tumor treatment. To ensure a safe and efficient operation of these applications, a noninvasive and accurate characterization of the particles is required. In this work, a magnetic characterization technique is presented in which the particles are excited by specific pulsed time-varying magnetic fields. This way, we can selectively excite nanoparticles of a given size so that the resulting measurement gives direct information on the size distribution without the need for any a priori assumptions or complex postprocessing procedures to decompose the measurement signal. This contrasts state-of-the-art magnetic characterization techniques. The possibility to selectively excite certain particle types opens up perspectives in “multicolor” particle imaging, where different particle types need to be imaged independently within one sample. Moreover, the presented methodology allows one to simultaneously determine the size-dependent coercivity of the particles. This is not only a valuable structure–property relation from a fundamental point of view, it is also practically relevant to optimize applications like magnetic particle hyperthermia. We numerically demonstrate that the novel characterization technique can accurately reconstruct several particle size distributions and is able to retrieve the coercivity–size relation of the particles. The developed technique advances current magnetic nanoparticle characterization possibilities and opens up exciting pathways for biomedical applications and particle imaging procedures

Balanced magnetic logic gates in a kagome spin ice

Nanomagnetic logic (NML) is a promising candidate to replace or complement traditional charged-based logic devices. Single NML gates such as the three-input majority gate are well studied, and their functionality has been verified experimentally. However, such gates suffer from a problem in that they sometimes produce erroneous output when integrated into circuits. A fundamental solution is offered by using balanced logic gates: gates for which the ground states corresponding to all possible input states have the same energy. We investigate how balanced gates can be created from kagome spin ice elements. We present a balanced NAND (and NOR) gate consisting of 19 dipole-coupled uniaxially anisotropic magnets. This gate can be either driven by an external clocking field or thermally driven. In the latter case, we numerically show that the gate has a reliability of at least 96%, a number which is shown to be robust against disorder. The presented gate provides a proof of concept for an artificial kagome spin ice NML gate

The design and verification of Mumax3

We report on the design, verification and performance of mumax3, an open-source GPU-accelerated micromagnetic simulation program. This software solves the time- and space dependent magnetization evolution in nano- to micro scale magnets using a finite-difference discretization. Its high performance and low memory requirements allow for large-scale simulations to be performed in limited time and on inexpensive hardware. We verified each part of the software by comparing results to analytical values where available and to micromagnetic standard problems. mumax3 also offers specific extensions like MFM image generation, moving simulation window, edge charge removal and material grains

Estimating the heating of complex nanoparticle aggregates for magnetic hyperthermia

Understanding and predicting the heat released by magnetic nanoparticles is central to magnetic hyperthermia treatment planning. In most cases, nanoparticles form aggregates when injected in living tissues, thereby altering their response to the applied alternating magnetic field and preventing the accurate prediction of the released heat. We performed a computational analysis to investigate the heat released by nanoparticle aggregates featuring different sizes and fractal geometry factors. By digitally mirroring aggregates seen in biological tissues, we found that the average heat released per particle stabilizes starting from moderately small aggregates, thereby facilitating making estimates for their larger counterparts. Additionally, we studied the heating performance of particle aggregates over a wide range of fractal parameters. We compared this result with the heat released by non-interacting nanoparticles to quantify the reduction of heating power after being instilled into tissues. This set of results can be used to estimate the expected heating in vivo based on the experimentally determined nanoparticle properties

Advanced analysis of magnetic nanoflower measurements to leverage their use in biomedicine

Magnetic nanoparticles are an important asset in many biomedical applications ranging from the local heating of tumours to targeted drug delivery towards diseased sites. Recently, magnetic nanoflowers showed a remarkable heating performance in hyperthermia experiments thanks to their complex structure leading to a broad range of magnetic dynamics. To grasp their full potential and to better understand the origin of this unexpected heating performance, we propose the use of Kaczmarz' algorithm in interpreting magnetic characterisation measurements. It has the advantage that no a priori assumptions need to be made on the particle size distribution, contrasting current magnetic interpretation methods that often assume a lognormal size distribution. Both approaches are compared on DC magnetometry, magnetorelaxometry and AC susceptibility characterisation measurements of the nanoflowers. We report that the lognormal distribution parameters vary significantly between data sets, whereas Kaczmarz' approach achieves a consistent and accurate characterisation for all measurement sets. Additionally, we introduce a methodology to use Kaczmarz' approach on distinct measurement data sets simultaneously. It has the advantage that the strengths of the individual characterisation techniques are combined and their weaknesses reduced, further improving characterisation accuracy. Our findings are important for biomedical applications as Kaczmarz' algorithm allows to pinpoint multiple, smaller peaks in the nanostructure's size distribution compared to the monomodal lognormal distribution. The smaller peaks permit to fine-tune biomedical applications with respect to these peaks to e.g. boost heating or to reduce blurring effects in images. Furthermore, the Kaczmarz algorithm allows for a standardised data analysis for a broad range of magnetic nanoparticle samples. Thus, our approach can improve the safety and efficiency of biomedical applications of magnetic nanoparticles, paving the way towards their clinical use