United States

We introduce flatspin, a novel simulator for artificial spin ices (ASI), i.e., ensembles of dipole coupled nanomagnets arranged in a deliberately constructed geometry. Understanding the complex behavior of these systems has provided insight into a wide range of phenomena [4]. Modeling the dynamics of ASI and their connection to the specific design has been paramount to the progression of ASI research [5]. However, conventional modeling techniques are often either too computationally costly or do not provide enough insight into the dynamics of the ASI. Monte Carlo methods approximating each magnet as a single dipole are useful in the search for low-energy configurations [1, 6], but capturing state transitions and dynamics observed in real systems is challenging. On the other hand, high-fidelity micromagnetic simulations are restricted to a small number of nanomagnets due to the computational cost. With the relatively small systems available through this approach, it is difficult to capture large-scale emergent behavior, e.g. [6]. With flatspin, we offer a different approach to the challenge of modeling, and aim to capture large-scale ASI behaviors. At the heart of flatspin lies a robust magnetic dipole model and a switching criteria based on a generalized Stoner-Wohlfarth model. GPU acceleration together with a model optimized for speed enables flatspin to simulate realistic dynamics of millions of magnets within practical time frames. We demonstrate the versatility of flatspin by modeling a diverse set of established experimental results. Reproduced results include emergent fine-scale patterns in kagome ASI [3], large-scale domain sizes in square ASI [6], and different magnetic ordering of square and pinwheel ASI [2] (see Fig. 1). Furthermore, magnetization details of pinwheel ASI during field-driven reversal have been reproduced, for the first time, by a dipole model. With its capability to model ASI dynamics at unprecedented speeds, we believe flatspin will be an asset to the ASI community.References: [1] X. Ke, J. Li, C. Nisoli et al., Physical Review Letters., Vol. 101(3), p.037205 (2008) [2] R. Macêdo, G. M. Macauley, F. S. Nascimento et al., Physical Review B., Vol. 98(1), p. 014437 (2018) [3] E. Mengotti, L. J. Heyderman, A. F. Rodríguez et al., Nature Physics., Vol. 7(1), p.68 (2011) [4] S. H. Skjærvø, C. H. Marrows, R. L. Stamps et al., Nature Reviews Physics., Vol. 2(1), p.13–28 (2020) [5] R. L. Stamps, Nature Physics., Vol. 10(9), p.623–624 (2014) [6] S. Zhang, I. Gilbert, C. Nisoli, Nature., Vol. 500(7464), p.553–557 (2013)

MMM 2020

Introducing Flatspin: Enabling Large Scale Dynamical Artificial Spin Ice Simulations

The 65th Annual Conference on Magnetism and Magnetic Materials (MMM 2020) was held in a virtual form. The COVID-19 pandemic, with its associated uncertainty about international travel and the ability to gather in large groups, meant that the Steering Committee decided to cancel the face-to-face meeting that had been planned in Palm Beach, Florida November 2-6, 2020.

We were reluctant to give up the benefits of meeting face-to-face, and know that shifting to a virtual conference is a significant change for this community. Nevertheless, we are first and foremost concerned about the health and safety of all our Conference participants and their loved ones on returning home after travelling.

The MMM 2020 Steering Committee was working exceptionally hard, along with our sponsors, the IEEE Magnetics Society and AIP Publishing, to make this event serve the needs of our participants, who live in time zones all around the globe, in the best possible way. 

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Introducing Flatspin: Enabling Large Scale Dynamical Artificial Spin Ice Simulations

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Chemical order-disorder transitions in intermetallic alloys for high-density magnetic recording

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