Publication date: Jan 08, 2024
We present a self-consistent method based on first-principles calculations to determine the magnetic ground state of materials, regardless of their dimensionality. Our methodology is founded on satisfying the stability conditions derived from the linear spin wave theory (LSWT) by optimizing the magnetic structure iteratively. We demonstrate the effectiveness of our method by successfully predicting the experimental magnetic structures of NiO, FePS₃, FeP, MnF₂, FeCl₂, and CuO. In each case, we compared our results with available experimental data and existing theoretical calculations reported in the literature. Finally, we discuss the validity of the method and the possible extensions.
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File name | Size | Description |
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NiO.tar.gz
MD5md5:02b5fac1c748ee9e612d6d803efaed71
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319.4 KiB | NiO ground state magnetic data |
FePS3.tar.gz
MD5md5:54ebb117ece8326050225bf6fc141d78
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5.5 MiB | FePS3 ground state magnetic data |
FeP.tar.gz
MD5md5:9ca0793b00444416ef5a7ea596c741e0
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4.5 MiB | FeP ground state magnetic data |
MnF2.tar.gz
MD5md5:b8d7b3f9e92fc7f7ccc24e770cf32c2f
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328.7 KiB | MnF2 ground state magnetic data |
FeCl2.tar.gz
MD5md5:653cd5334436fb3b69e43352704083bf
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1.3 MiB | FeCl2 ground state magnetic data |
CuO.tar.gz
MD5md5:b57a770ecd719fe8e9713b727fb1290b
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6.2 MiB | CuO ground state magnetic data |
CrSBr.tar.gz
MD5md5:6f28fff6697971647c20b590529abcb0
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591.0 KiB | CrSBr ground state magnetic data |
Mn5Si3.tar.gz
MD5md5:07762323b9a3b0de80c043a2e5f7e34c
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32.0 MiB | Mn5Si3 ground state magnetic data |
TB2J_yaml.py
MD5md5:6d73b882212a0fbc84e156f454ac851c
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624 Bytes | Python script to generate SpinIO object and use the TB2J package utilities |
2024.5 (version v1) [This version] | Jan 08, 2024 | DOI10.24435/materialscloud:5m-2t |