論文 Intrinsic magnetic properties for SmFe 12−xTx thin films via high-throughput experiments and machine learning techniques

Daisuke Ogawa SAMURAI ORCID (National Institute for Materials Science) ; Ryotaro Akagi ; Keitaro Sodeyama SAMURAI ORCID (National Institute for Materials Science) ; Yukiko K. Takahashi SAMURAI ORCID (National Institute for Materials Science)

コレクション

引用
Daisuke Ogawa, Ryotaro Akagi, Keitaro Sodeyama, Yukiko K. Takahashi. Intrinsic magnetic properties for SmFe 12−xTx thin films via high-throughput experiments and machine learning techniques. Science and Technology of Advanced Materials: Methods. 2025, 5 (1), 2554572. https://doi.org/10.1080/27660400.2025.2554572

説明:

(abstract)

The development of next-generation permanent magnets has become critical due to the limited performance improvements in Nd-Fe-B magnets and concerns over rare earth supply. ThMn12-type rare-earth intermetallic compounds have emerged as promising alternatives, offering superior performance and reduced rare earth content. This study systematically investigates the magnetic properties of Sm(Fe12−xTx)-based thin films synthesized via combinatorial sputtering. Various stabilizing elements (e.g., Ti, V, Co, Cr) were analyzed to explore their effects on phase stability, saturation magnetization (μ0Ms), anisotropy field (μ0Hs), and Curie temperature (Tc). High-throughput structural and magnetic characterizations, coupled with machine learning (ML) predictions, facilitated efficient data acquisition and analysis. Experimental results reaffirmed trends such as μ0Ms enhancement with Co and phase-stabilization capabilities of Ti and V. Novel insights into additives like Cr and Ta revealed potential Tc improvements. ML regression models (Random Forest and XGBoost) identified electronegativity as a key factor influencing μ0Ms. Predictive analyses successfully estimated μ0Ms trends and ThMn12 phase stability for unexplored compositions, enhancing the active learning framework for material discovery. This work highlights the synergy of combinatorial deposition, high-throughput data collection, and ML-assisted prediction in accelerating the exploration of magnetic materials. Future extensions to multi-element systems and other magnetic phases are expected to expedite the discovery of high-performance magnets for motors and energy applications.

権利情報:

キーワード: SmFe12

刊行年月日: 2025-12-31

出版者: Informa UK Limited

掲載誌:

  • Science and Technology of Advanced Materials: Methods (ISSN: 27660400) vol. 5 issue. 1 2554572

研究助成金:

  • JST CREST program JPMJC22C3
  • Material Research and Development Project JPMXP1122715503

原稿種別: 著者最終稿 (Accepted manuscript)

MDR DOI: https://doi.org/10.48505/nims.6051

公開URL: https://doi.org/10.1080/27660400.2025.2554572

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更新時刻: 2025-12-23 09:39:56 +0900

MDRでの公開時刻: 2025-12-23 12:19:51 +0900

ファイル名 サイズ
ファイル名 STAM Method Manuscript submit final.pdf (サムネイル)
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サイズ 1.2MB 詳細
ファイル名 STAM Method SI submit final.pdf
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サイズ 163KB 詳細