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

doi:10.1038/s41524-025-01757-5

論文 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

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Ryo Toyama, Yuma Iwasaki, Prabhanjan D. Kulkarni, Hirofumi Suto, Tomoya Nakatani, Yuya Sakuraba. High-throughput materials exploration system for the anomalous Hall effect using combinatorial experiments and machine learning. npj Computational Materials. 2025, 11 (1), 269. https://doi.org/10.1038/s41524-025-01757-5

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(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.

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