Rotational symmetry in nanodisks drives size-dependent transitions among ferromagnetic, skyrmion, skyrmionium, and multi-skyrmion states, while square and rectangular geometries suppress topological complexity via corner demagnetization but stabilize skyrmions across broader field and size ranges.
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A neural network maps one image to a chiral spin texture whose skyrmion number equals the Euler characteristic, refined by exchange, DM, and anisotropy terms in the loss.
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Geometric symmetry and size-dependent skyrmion phase transitions in magnetic nanostructures
Rotational symmetry in nanodisks drives size-dependent transitions among ferromagnetic, skyrmion, skyrmionium, and multi-skyrmion states, while square and rectangular geometries suppress topological complexity via corner demagnetization but stabilize skyrmions across broader field and size ranges.
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Predicting Euler Characteristics and Constructing Topological Structure Using Machine Learning Techniques
A neural network maps one image to a chiral spin texture whose skyrmion number equals the Euler characteristic, refined by exchange, DM, and anisotropy terms in the loss.