Anomaly detection of fermi surface morphology in Co2MnGaxGe1-x via interpretable machine learning

The Fermi surface provides indispensable insights into the electronic structure of materials. Here, we present a robust analysis framework employing an interpretable machine learning approach to investigate Fermi surfaces. Complex Fermi surface images of the Heusler alloy Co2MnGaxGe1−x, along with corresponding spin polarization, were analyzed using simple principal component analysis (PCA). Our results reveal that pronounced “jumps” in the PCA space correlate strongly with extrema and inflection points in the spin polarization. Notably, compositions near Ga = 0.94–0.95 exhibit significant changes attributable to the emergence of nodal lines. And the position of nodal lines in momentum space were automatically detected by differential analysis of outlier. Robustness evaluations demonstrate that our method remains effective even under conditions of increased image broadening and noise, mimicking ARPES experimental data. This method can contribute to the analysis of large-scale datasets by detecting non-systematic outliers.

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Publication Details

Journal
Scientific Reports
Published
2026-04-27
DOI
https://doi.org/10.1038/s41598-026-39115-0
Primary Topic
Heusler alloys: electronic and magnetic properties
Type
article
Field-Weighted Citation Impact
0.00

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article

Anomaly detection of fermi surface morphology in Co2MnGaxGe1-x via interpretable machine learning

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Scientific Reports
Heusler alloys: electronic and magnetic properties
article

Anomaly detection of fermi surface morphology in Co2MnGaxGe1-x via interpretable machine learning

Yuya Sakuraba, Koichiro Yaji, Naoka Nagamura, Alexandre Lira Foggiatto, Kentaro Fuku, Masato Kotsugi, Yuma Iwasaki, Takahiro Yamazaki, Yoshio Miura, Yasuhiko Igarashi, Daichi Ishikawa
article en

Abstract

The Fermi surface provides indispensable insights into the electronic structure of materials. Here, we present a robust analysis framework employing an interpretable machine learning approach to investigate Fermi surfaces. Complex Fermi surface images of the Heusler alloy Co2MnGaxGe1−x, along with corresponding spin polarization, were analyzed using simple principal component analysis (PCA). Our results reveal that pronounced “jumps” in the PCA space correlate strongly with extrema and inflection points in the spin polarization. Notably, compositions near Ga = 0.94–0.95 exhibit significant changes attributable to the emergence of nodal lines. And the position of nodal lines in momentum space were automatically detected by differential analysis of outlier. Robustness evaluations demonstrate that our method remains effective even under conditions of increased image broadening and noise, mimicking ARPES experimental data. This method can contribute to the analysis of large-scale datasets by detecting non-systematic outliers.

Scientific ReportsVol. 16(1)
University of Tsukuba (JP), Tokyo University of Technology (JP), Tokyo University of Science (JP), Tohoku University (JP), National Institute for Materials Science (JP), Kyoto Institute of Technology (JP), Tohoku University Hospital (JP), Nagoya University (JP)
Japan Society for the Promotion of Science, Core Research for Evolutional Science and Technology
Openalex Percentile: Top 17%
Heusler alloys: electronic and magnetic properties
DOIOpen Access PDFOpenAlex
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