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PreprintCC BY 4.0arXiv

Large spin splitting metallic altermagnets from machine-learned design rules

Abstract

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 A2XY 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 Co2AlSc (Δmax=2.24~eV) and Fe2AlGe ($2.07$~eV). A Julliere-model estimate gives a tunneling magnetoresistance of up to 203% at the Fermi level for Co2AlSc. These results identify a chemically tunable family of metallic d-wave altermagnets and demonstrate how interpretable machine learning can guide targeted first-principles searches.

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BibTeX
@misc{sufyan2026large,
  title = {{Large spin splitting metallic altermagnets from machine-learned design rules}},
  author = {Ali Sufyan and Brahim Marfoua and J. Andreas Larsson and Rickard Armiento and Erik van Loon},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2609.14051},
  eprint = {2609.14051},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.14051},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Large spin splitting metallic altermagnets from machine-learned design rules
AU  - Ali Sufyan
AU  - Brahim Marfoua
AU  - J. Andreas Larsson
AU  - Rickard Armiento
AU  - Erik van Loon
PY  - 2026
DA  - 2026/10/08
PB  - arXiv
DO  - 10.48550/arxiv.2609.14051
UR  - https://arxiv.org/abs/2609.14051
AB  - 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_2AlSc (Δ_max=2.24~eV) and Fe_2AlGe ($2.07$~eV). A Julliere-model estimate gives a tunneling magnetoresistance of up to 203% at the Fermi level for Co_2AlSc. These results identify a chemically tunable family of metallic d-wave altermagnets and demonstrate how interpretable machine learning can guide targeted first-principles searches.
ER  -