Scholarlink

<p>Nested skyrmion bags are topological magnetic structures with tunable topological charge, offering potential for spintronic applications. Predicting their current-driven dynamics, particularly the skyrmion Hall angle, across diverse structural and material parameters remains challenging due to the complexity of the underlying physics. Here we show, through micromagnetic simulations validated by Thiele equation analysis, that zero-topological-charge bags move linearly without transverse deflection, while non-zero-charge configurations exhibit a widely tunable Hall angle. Transverse elongation at high nesting levels can be suppressed by additional domain wall layers. To enable rapid prediction, we implement twelve machine learning models, among which gradient boosting methods and neural networks achieve high accuracy, whereas linear regression fails, confirming the inherent nonlinearity of the system. Leveraging this predictive capability, we demonstrate a demultiplexer device that routes information based on the Hall angle. This work provides a framework for designing topology-based spintronic devices such as racetrack memory and signal routers.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Machine learning assisted prediction of dynamics in current-driven nested skyrmion bags

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月1日

查看全文
  • Rui Li,
  • Yuge Zhu,
  • Xinyu Zhang,
  • Mengting Li,
  • Xingqiang Shi,
  • Ruining Wang,
  • Jianglong Wang,
  • Hu Zhang,
  • Penglai Gong,
  • Chendong Jin

摘要

Nested skyrmion bags are topological magnetic structures with tunable topological charge, offering potential for spintronic applications. Predicting their current-driven dynamics, particularly the skyrmion Hall angle, across diverse structural and material parameters remains challenging due to the complexity of the underlying physics. Here we show, through micromagnetic simulations validated by Thiele equation analysis, that zero-topological-charge bags move linearly without transverse deflection, while non-zero-charge configurations exhibit a widely tunable Hall angle. Transverse elongation at high nesting levels can be suppressed by additional domain wall layers. To enable rapid prediction, we implement twelve machine learning models, among which gradient boosting methods and neural networks achieve high accuracy, whereas linear regression fails, confirming the inherent nonlinearity of the system. Leveraging this predictive capability, we demonstrate a demultiplexer device that routes information based on the Hall angle. This work provides a framework for designing topology-based spintronic devices such as racetrack memory and signal routers.