Robust ML framework speeds up Fermi surface screening for spintronic Heusler alloys
Researchers from the Tokyo University of Science, Kyoto Institute of Technology, University of Tsukuba and National Institute for Materials Science (NIMS) have developed an interpretable machine-learning framework that automatically detects anomalies in Fermi surface maps of the spintronic Heusler alloy Co₂MnGaₓGe₁₋ₓ (CMGG). The approach uses principal component analysis (PCA) on simulated Fermi-surface images to pinpoint compositions where the electronic structure changes sharply, and links these anomalies directly to nodal-line formation and variations in spin polarization.
In this work, the team focuses on CMGG, a Heusler alloy with half-metallicity, nodal-line features and high spin polarization, known for its anomalous Nernst effect arising from nodal lines on the Fermi surface. Using density functional theory (DFT), they first generate a composition-dependent band-structure dataset and extract kₓ–kᵧ Fermi-surface cuts through the Γ point. These images are blurred to roughly approximate ARPES data, then converted into one-dimensional vectors and analyzed via PCA to obtain a low-dimensional representation where each point corresponds to a specific Ga content x.
Source: https://www.spintronics-info.com/robust-ml-framework-speeds-fermi-surface-screening-spintronic-heusler-alloys
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