従来比30倍の高速実験で新しい磁気センサー材料を発見～AIによるデータ解析・予測による超効率的開発に成功～

2025-09-04 物質・材料研究機構,科学技術振興機構

Web要約 の発言：

図: Fe_1-xX_x 組成傾斜薄膜のマルチチャンネル同時測定による異常ホール効果の高速計測。1組成あたり0.2時間で測定でき、従来手法に比べて約30倍の高速化を実現。機械学習により予測されたFe–Ir–Pt系で従来の最大値を更新。

＜関連情報＞

• https://www.nims.go.jp/press/2025/09/202509040.html
• https://www.nims.go.jp/press/2025/09/nhidm20000004hpn-att/202509040.pdf
• https://www.nature.com/articles/s41524-025-01757-5

組み合わせ実験と機械学習を用いた異常ホール効果の高スループット材料探索システム High-throughput materials exploration system for the anomalous Hall effect using combinatorial experiments and machine learning

Ryo Toyama,Yuma Iwasaki,Prabhanjan D. Kulkarni,Hirofumi Suto,Tomoya Nakatani & Yuya Sakuraba
npj Computational Materials  Published:03 September 2025
DOI:https://doi.org/10.1038/s41524-025-01757-5

Abstract

The development of new materials exhibiting large anomalous Hall effect (AHE) is essential for realizing highly efficient spintronic devices. However, this development has been a time-consuming process due to the combinatorial explosion for multielement systems and limited experimental throughput. In this study, we identify new materials exhibiting large AHE in heavy-metal-substituted Fe-based alloys using a high-throughput materials exploration method that combines deposition of composition-spread films using combinatorial sputtering, photoresist-free facile multiple-device fabrication using laser patterning, simultaneous AHE measurement of multiple devices using a customized multichannel probe, and prediction of candidate materials using machine learning. Based on experimental AHE data on Fe-based binary system alloyed with various single heavy metals, we perform machine learning analysis to predict the Fe–based ternary system containing two heavy metals for larger AHE. We experimentally confirm larger AHE in the predicted Fe–Ir–Pt system. Using scaling analysis, we reveal that the enhancement of AHE originates from the extrinsic contribution.