Scholarlink

<p>Frustrated magnetic systems such as spin ice are key platforms for novel metamaterials. However, identifying their ground states in finite arrays is a formidable challenge, as boundary sensitivity and metastable states trap conventional optimization methods. We introduce a virtuous-cycle AI pipeline where a genetic algorithm explores the latent space of a variational autoencoder (VAE), with the best candidates progressively refining the VAE’s representation. Applied to Kagome spin ice, this method reveals how the boundary magnetism is determined: boundaries break the symmetry of the <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\sqrt{3\,}\times \sqrt{3\,}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <msqrt> <mrow> <mn>3</mn> <mspace width="0.25em" /> </mrow> </msqrt> <mo>×</mo> <msqrt> <mrow> <mn>3</mn> <mspace width="0.25em" /> </mrow> </msqrt> </mrow> </math></EquationSource> </InlineEquation> magnetic superstructure while the bulk superstructure order in the interior maintains. Furthermore, it demonstrates that high geometric confinement induces a novel quasi-ferromagnetic phase, which breaks the interior superstructure order. Our work provides a predictive framework for designing frustrated materials and demonstrates a powerful AI approach for boundary-sensitive physical systems.</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

Boundary sensitivity in finite-sized artificial spin ice explored via AI-assisted genetic algorithms

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月21日

查看全文
  • Tae Jung Moon,
  • Seong Min Park,
  • Han Gyu Yoon,
  • Hee Young Kwon,
  • Changyeon Won

摘要

Frustrated magnetic systems such as spin ice are key platforms for novel metamaterials. However, identifying their ground states in finite arrays is a formidable challenge, as boundary sensitivity and metastable states trap conventional optimization methods. We introduce a virtuous-cycle AI pipeline where a genetic algorithm explores the latent space of a variational autoencoder (VAE), with the best candidates progressively refining the VAE’s representation. Applied to Kagome spin ice, this method reveals how the boundary magnetism is determined: boundaries break the symmetry of the \(\sqrt{3\,}\times \sqrt{3\,}\) 3 × 3 magnetic superstructure while the bulk superstructure order in the interior maintains. Furthermore, it demonstrates that high geometric confinement induces a novel quasi-ferromagnetic phase, which breaks the interior superstructure order. Our work provides a predictive framework for designing frustrated materials and demonstrates a powerful AI approach for boundary-sensitive physical systems.