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説明:

Autonomous high-throughput combinatorial experimentation is a key approach for accelerating materials discovery. However, achieving a fully closed-loop system remains a challenge due to the lack of effective optimization strategies for combinatorial experimentation. Here, we developed a Bayesian optimization method specifically designed for composition-spread films, enabling the selection of promising composition-spread films and identifying which elements should be compositionally graded. Using this approach, we demonstrated an autonomous closed-loop exploration of composition-spread films to enhance the anomalous Hall effect (AHE). Our method optimized the composition of a five-element alloy system consisting of three 3d ferromagnetic elements of Fe, Co, and Ni and two 5d heavy elements from Ta, W, or Ir to maximize the AHE. Through our autonomous exploration, we achieved a maximum anomalous Hall resistivity of 10.9 µΩ cm in Fe44.9Co27.9Ni12.1Ta3.3Ir11.7 amorphous thin film on thermally oxidized Si substrates deposited at room temperature.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: Machine learning, Autonomous, Combinatorial, Anomalous Hall effect

刊行年月日: 2025-11-19

出版者: Springer Science and Business Media LLC

掲載誌:

• npj Computational Materials (ISSN: 20573960) vol. 11 issue. 1 329

研究助成金:

• Japan Science and Technology Agency JPMJPR24T8
• Japan Science and Technology Agency JPMJCR21O1
• Japan Science and Technology Agency JPMJCR21O1
• Ministry of Education, Culture, Sports, Science and Technology JPMXP1121467561
• Ministry of Education, Culture, Sports, Science and Technology JPMXP1122715503
• Japan Society for the Promotion of Science JP21H01608

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1038/s41524-025-01828-7

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更新時刻: 2025-12-25 17:32:06 +0900

MDRでの公開時刻: 2025-12-26 08:19:24 +0900