Machine Learning Prediction of Magnetic Proximity Effect in van der Waals Heterostructures: From Atoms to Moiré

URN to cite this document:: urn:nbn:de:bvb:355-epub-779133
DOI to cite this document:: 10.5283/epub.77913

Cvitkovich, Lukas ; Zollner, Klaus

; Fabian, Jaroslav

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Date of publication of this fulltext: 18 Nov 2025 07:04

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Abstract

We introduce a machine learning framework that efficiently predicts large-scale proximity-induced magnetism in van der Waals heterostructures, overcoming the high computational cost of density functional theory (DFT). We apply it to graphene/Cr2Ge2Te6, which exhibits a previously unrecognized dichotomy. Unlike the spin polarization at the Fermi level, which follows the pseudospin, the ...