基于机器学习设计规则的大自旋劈裂金属性交错磁体

中文摘要 AI 辅助

交错磁体将补偿磁序与动量依赖的自旋劈裂相结合，在不产生净磁化的情况下提供自旋极化电子态。金属性d波交错磁体尤其具有前景，因为它们在线性响应中可以支持时间反演奇自旋电流，然而经过实验验证的块体实现仍然稀缺。在此，我们利用高通量密度泛函理论数据，将交错磁体能带劈裂的幅度与成分、结构及DFT衍生的磁性描述符关联起来。一个可解释的梯度提升模型确定了两个候选优先级排序标准：紧凑的晶胞和能够维持可观局域磁矩的磁性亚晶格。在这些趋势的指导下，我们筛选了空间群为P4/mmm的四方A2XY Heusler化合物。在307个结构中，对称性识别出169个交错磁排列，其中157个在DFT中仍为金属性交错磁体。16个实现了交错磁共线基态，其中15个具有动力学和力学稳定性，10个还位于或低于计算的热力学凸包。六个候选者超过了在同一计算协议下获得的CrSb劈裂，其中以Co2AlSc（Δmax=2.24 eV）和Fe2AlGe（2.07 eV）为首。Julliere模型估计Co2AlSc在费米能级处的隧穿磁电阻高达203%。这些结果确定了一个化学可调的金属性d波交错磁体家族，并展示了可解释机器学习如何指导有针对性的第一性原理搜索。

英文摘要

Altermagnets combine compensated magnetic order with momentum-dependent spin splitting, providing spin-polarized electronic states without a net magnetization. Metallic $d$-wave altermagnets are particularly promising because they can support time-reversal-odd spin currents in linear response, yet experimentally validated bulk realizations remain scarce. Here, we use high-throughput density-functional-theory data to relate the magnitude of altermagnetic band splitting to compositional, structural, and DFT-derived magnetic descriptors. An interpretable gradient-boosted model identifies two candidate-prioritization criteria: compact unit cells and magnetic sublattices that sustain sizable local moments. Guided by these trends, we screen tetragonal $A_2XY$ Heusler compounds in space group $P4/mmm$. Among 307 structures, symmetry identifies 169 altermagnetic arrangements, of which 157 remain metallic altermagnets in DFT. Sixteen realize an altermagnetic collinear ground state, of which 15 are dynamically and mechanically stable and 10 also lie on or below the calculated thermodynamic hull. Six candidates exceed the CrSb splitting obtained under the same computational protocol, led by Co$_2$AlSc ($Δ_\mathrm{max}=2.24$~eV) and Fe$_2$AlGe ($2.07$~eV). A Julliere-model estimate gives a tunneling magnetoresistance of up to $203\%$ at the Fermi level for Co$_2$AlSc. These results identify a chemically tunable family of metallic $d$-wave altermagnets and demonstrate how interpretable machine learning can guide targeted first-principles searches.

发表机构

• Lund University（隆德大学）
• Linköping University（林雪平大学）
• Luleå University of Technology（吕勒奥理工大学）

机构由 AI 辅助整理，请以论文原文为准。