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

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.