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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Online Access: | https://arxiv.org/abs/2505.18620 |
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| _version_ | 1866909622096363520 |
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| author | Wu, Hao Agterberg, Daniel F. |
| author_facet | Wu, Hao Agterberg, Daniel F. |
| contents | Parity-time-reversal-symmetric odd-parity antiferromagnetic (AFM1) materials are of interest for their symmetry-enabled quantum transport and optical effects. These materials host odd-parity terms in their band dispersion, leading to asymmetric energy bands and enabling responses such as the magnetopiezoelectric effect, nonreciprocal conductivity, and photocurrent generation. In addition, they may support a nonlinear spin Hall effect without spin-orbit coupling, offering an efficient route to spin current generation. We identify 23 candidate AFM1 materials by combining artificial intelligence, density functional theory (DFT), and symmetry analysis. Using a graph neural network model and incorporating AFM1-specific symmetry constraints, we screen Materials Project compounds for high-probability AFM1 candidates. DFT calculations show that AFM1 has the lowest energy among the tested magnetic configurations in 23 candidate materials. These include 3 experimentally verified AFM1 materials, 10 synthesized compounds with unknown magnetic structures, and 10 that are not yet synthesized. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18620 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-predicted PT-symmetric magnets Wu, Hao Agterberg, Daniel F. Materials Science Parity-time-reversal-symmetric odd-parity antiferromagnetic (AFM1) materials are of interest for their symmetry-enabled quantum transport and optical effects. These materials host odd-parity terms in their band dispersion, leading to asymmetric energy bands and enabling responses such as the magnetopiezoelectric effect, nonreciprocal conductivity, and photocurrent generation. In addition, they may support a nonlinear spin Hall effect without spin-orbit coupling, offering an efficient route to spin current generation. We identify 23 candidate AFM1 materials by combining artificial intelligence, density functional theory (DFT), and symmetry analysis. Using a graph neural network model and incorporating AFM1-specific symmetry constraints, we screen Materials Project compounds for high-probability AFM1 candidates. DFT calculations show that AFM1 has the lowest energy among the tested magnetic configurations in 23 candidate materials. These include 3 experimentally verified AFM1 materials, 10 synthesized compounds with unknown magnetic structures, and 10 that are not yet synthesized. |
| title | AI-predicted PT-symmetric magnets |
| topic | Materials Science |
| url | https://arxiv.org/abs/2505.18620 |