DFT-aided machine learning-based discovery of magnetism in Fe-based bimetallic chalcogenides.

ABSTRACT: 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, Fe_xA_yB; A represents Ni, Co, Cr, or Mn, and B represents S, Se, or Te, and x and y represent the concentration

SUBMITTER: Pant D 

PROVIDER: S-EPMC9968303 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature