Ryo Toyama
;
Ryo Tamura
;
Shoichi Matsuda
;
Yuma Iwasaki
;
Yuya Sakuraba
Description:
(abstract)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.
Rights:
Keyword: Machine learning, Autonomous, Combinatorial, Anomalous Hall effect
Date published: 2025-11-19
Publisher: Springer Science and Business Media LLC
Journal:
Funding:
Manuscript type: Publisher's version (Version of record)
MDR DOI:
First published URL: https://doi.org/10.1038/s41524-025-01828-7
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Updated at: 2025-12-25 17:32:06 +0900
Published on MDR: 2025-12-26 08:19:24 +0900
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