Twisted bilayer graphene exhibits many intriguing behaviors ranging from superconductivity to the anomalous Hall effect to ferromagnetism at a magic angle of ∼1°. Here, using a first-principles approach, we reveal ferromagnetism in a twisted bilayer graphene nanoflex. Our results demonstrate that when the energy gap of a twisted nanoflex approaches zero, electronic instability occurs and a ferromagnetic gap state emerges spontaneously to lower the energy. Unlike the observed ferromagnetism at a magic angle in the graphene bilayer, we notice the ferromagnetic phase appearing aperiodically between 0 and 30° in the twisted nanoflex. The origin of electronic instability at various twist angles is ascribed to the several higher-symmetry phases that are broken to lower the energy resulting from an aperiodic modulation of the interlayer interaction in the nanoflex. Besides unraveling a spin-pairing mechanism for the reappearance of the nonmagnetic phase, we have found orbitals at the boundary of nanoflex contributing to ferromagnetism.

The electronic properties of a bilayer graphene nanoflex (BLGNF) depend sensitively upon the strength of the inter-layer electronic coupling (IEC) energy. Upon tuning the IEC via changing the twist angle...

With the technological advancement in recent years and the widespread use of magnetism in every sector of the current technology, a search for a low-cost magnetic material has been more important than ever. The discovery of magnetism in alternate materials such as metal chalcogenides with abundant atomic constituents would be a milestone in such a scenario. However, considering the multitude of possible chalcogenide configurations, predictive computational modeling or experimental synthesis is an open challenge. Here, we recourse to a stacked generalization machine learning model to predict magnetic moment (µB) in hexagonal Fe-based bimetallic chalcogenides, FexAyB; A represents Ni, Co, Cr, or Mn, and B represents S, Se, or Te, and x and y represent the concentration of respective atoms. The stacked generalization model is trained on the dataset obtained using first-principles density functional theory. The model achieves MSE, MAE, and R2 values of 1.655 (µB)2, 0.546 (µB), and 0.922 respectively on an independent test set, indicating that our model predicts the compositional dependent magnetism in bimetallic chalcogenides with a high degree of accuracy. A generalized algorithm is also developed to test the universality of our proposed model for any concentration of Ni, Co, Cr, or Mn up to 62.5% in bimetallic chalcogenides.

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