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Title: Investigating magnetic van der Waals materials using data-driven approaches
In this work, we investigate magnetic monolayers of the form A i A ii B 4 X 8 based on the well-known intrinsic topological magnetic van der Waals (vdW) material MnBi 2 Te 4 (MBT) using first-principles calculations and machine learning techniques. We select an initial subset of structures to calculate the thermodynamic properties, electronic properties, such as the band gap, and magnetic properties, such as the magnetic moment and magnetic order using density functional theory (DFT). Data analytics approaches are used to gain insight into the microscopic origin of materials’ properties. The dependence of materials’ properties on chemical composition is also explored. For example, we find that the formation energy and magnetic moment depend largely on A and B sites whereas the band gap depends on all three sites. Finally, we employ machine learning tools to accelerate the search for novel vdW magnets in the MBT family with optimized properties. This study creates avenues for rapidly predicting novel materials with desirable properties that could enable applications in spintronics, optoelectronics, and quantum computing.  more » « less
Award ID(s):
2044842
PAR ID:
10413397
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Journal of Materials Chemistry C
Volume:
11
Issue:
17
ISSN:
2050-7526
Page Range / eLocation ID:
5601 to 5610
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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