在伊特铁石榴石薄膜中重新审视低频铁磁共振
A Napierała-Batygolska1, P Tomczak2
1Faculty of Physics, Adam Mickiewicz University, ul. Uniwersytetu Poznańakiego 2, 61-614, Poznań, Poland. aleksandra.batygolska@amu.edu.pl.
Scientific reports
|April 2, 2025
概括
铁磁共振 (FMR) 实验现在将磁能纳入景观曲率. 机器学习揭示了YIG薄膜中的频率依赖系数,完善了FMR数据的解释.
科学领域:
- 材料科学 材料科学 材料科学
- 凝聚物质物理学 凝聚物质物理学
- 磁力学 磁力学 是一种
背景情况:
- 铁磁共振 (FMR) 实验对于描述磁性材料至关重要.
- 传统的FMR数据解释依赖于固定的无磁性和去磁化常数.
- 最近的进展凸显了磁性自由能量景观曲率的重要性.
研究的目的:
- 为了重新解释在伊铁石榴石 (YIG) 薄膜上的低频FMR测量.
- 调查磁性自由能量景观曲率在FMR数据中的作用.
- 使用机器学习探索能量系数的频率依赖.
主要方法:
- 磁性自由能量景观曲率概念应用于FMR数据.
- 在面向[111]的YIG薄膜上进行低频FMR测量.
- 机器学习算法分析FMR数据和能量系数.
主要成果:
- 在磁性自由能量膨胀中的系数观察到的频率依赖.
- 证明这些系数可能随着共振频率的变化而变化.
- 质疑了固定的无磁性和去磁化常数的假设.
结论:
- 磁性自由能景观的曲率对于准确的FMR解释至关重要.
- 频率依赖系数提供了对FMR实验输出的更精确的理解.
- 这种方法对未来的FMR研究和材料表征有重大影响.
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