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<p>Non-flat energy landscapes leading to localized pinning of skyrmions pose an unavoidable challenge for studies of fundamental 2D spin structure dynamics and applications. Accounting for pinning is a key requirement for predictive modeling of skyrmion systems, impacting the system’s dynamics and introducing randomizing effects. We image skyrmions using magneto-optical Kerr microscopy on a magnetic thin film and analyze their hopping dynamics within the non-flat energy landscape. To achieve a fully quantitative model, we utilize diffusion and dwell times at pinning sites in both experiment and a coarse-grained Thiele model to determine simulation parameters and extrapolate the pinning energy landscape into regions that cannot be sampled within reasonable experimental timespans. We show a direct conversion between simulation and experimental units, the missing key step previously preventing quantitative quasiparticle modeling. We demonstrate our approach’s predictive power and ability for predictive in-silico prototyping of skyrmion devices by measuring the density dependence of skyrmion diffusion, showing excellent agreement with simulation predictions.</p>

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Real-Time Modeling of Skyrmion Dynamics in Arbitrary 2D Spatially Dependent Pinning Potential Landscapes

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月22日

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  • Simon M. Fröhlich,
  • Tobias Sparmann,
  • Maarten A. Brems,
  • Jan Rothörl,
  • Fabian Kammerbauer,
  • Klaus Raab,
  • Sachin Krishnia,
  • Mathias Kläui,
  • Peter Virnau

摘要

Non-flat energy landscapes leading to localized pinning of skyrmions pose an unavoidable challenge for studies of fundamental 2D spin structure dynamics and applications. Accounting for pinning is a key requirement for predictive modeling of skyrmion systems, impacting the system’s dynamics and introducing randomizing effects. We image skyrmions using magneto-optical Kerr microscopy on a magnetic thin film and analyze their hopping dynamics within the non-flat energy landscape. To achieve a fully quantitative model, we utilize diffusion and dwell times at pinning sites in both experiment and a coarse-grained Thiele model to determine simulation parameters and extrapolate the pinning energy landscape into regions that cannot be sampled within reasonable experimental timespans. We show a direct conversion between simulation and experimental units, the missing key step previously preventing quantitative quasiparticle modeling. We demonstrate our approach’s predictive power and ability for predictive in-silico prototyping of skyrmion devices by measuring the density dependence of skyrmion diffusion, showing excellent agreement with simulation predictions.