Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2510.18469 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915566557593600 |
|---|---|
| author | Morée, Jean-Baptiste Bouaziz, Juba Arita, Ryotaro |
| author_facet | Morée, Jean-Baptiste Bouaziz, Juba Arita, Ryotaro |
| contents | We employ interpretable explicit machine learning to analyze the material dependence of the magnetic transition temperature $T_c$ in ferromagnetic and ferrimagnetic Heusler compounds. For around 200 compounds, we consider both experimental $T_c$ and calculated $T_c$ using \textit{ab initio} determination of magnetic interactions together with a Monte-Carlo solution. We use the hierarchical dependence extraction (HDE) procedure [Morée and Arita, Phys. Rev. B 110, 014502 (2024)] to extract the dependencies of $T_c$ on chemical proportions and magnetic moments from the main order to the higher order, and construct an explicit expression of $T_c$ from these dependencies. The main results are: (a) $T_c$ is mainly controlled by the proportions of Fe, Co, and Mn, and increases with these proportions, consistent with previous machine learning analyses of ferromagnetic materials. (b) The HDE describes $T_c$ with an accuracy that is comparable to that of other machine learning procedures. (c) The HDE expression of $T_c$ can be interpreted as a generalized order parameter that increases with increasing magnetization amplitude, in qualitative agreement with various theories of phase transitions. These results strengthen our understanding of the material dependence of $T_c$ in collinear Heusler magnets and motivate the further use of HDE in material design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_18469 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Uncovering critical temperature dependence in Heusler magnets via explicit machine learning Morée, Jean-Baptiste Bouaziz, Juba Arita, Ryotaro Materials Science We employ interpretable explicit machine learning to analyze the material dependence of the magnetic transition temperature $T_c$ in ferromagnetic and ferrimagnetic Heusler compounds. For around 200 compounds, we consider both experimental $T_c$ and calculated $T_c$ using \textit{ab initio} determination of magnetic interactions together with a Monte-Carlo solution. We use the hierarchical dependence extraction (HDE) procedure [Morée and Arita, Phys. Rev. B 110, 014502 (2024)] to extract the dependencies of $T_c$ on chemical proportions and magnetic moments from the main order to the higher order, and construct an explicit expression of $T_c$ from these dependencies. The main results are: (a) $T_c$ is mainly controlled by the proportions of Fe, Co, and Mn, and increases with these proportions, consistent with previous machine learning analyses of ferromagnetic materials. (b) The HDE describes $T_c$ with an accuracy that is comparable to that of other machine learning procedures. (c) The HDE expression of $T_c$ can be interpreted as a generalized order parameter that increases with increasing magnetization amplitude, in qualitative agreement with various theories of phase transitions. These results strengthen our understanding of the material dependence of $T_c$ in collinear Heusler magnets and motivate the further use of HDE in material design. |
| title | Uncovering critical temperature dependence in Heusler magnets via explicit machine learning |
| topic | Materials Science |
| url | https://arxiv.org/abs/2510.18469 |