Data-Driven Studies of van der Waals Magnetic Heterostructures.

Journal: ACS applied materials & interfaces
Published Date: Aug 18, 2025

Abstract

Magnetic van der Waals (vdW) materials have the potential to revolutionize the semiconductor industry due to their various exotic physical properties. Herein, we use a data-driven framework to investigate magnetic vdW heterostructures of the form AABX/BX based on a well-known heterostructure MnBiTe/SbTe. Our study shows that combining a nonmagnetic BX monolayer with a magnetic AABX monolayer can alter the magnetic properties as well as the band gap. By training various machine learning (ML) models on the density functional theory (DFT)-generated data set, we rapidly predict the properties of 16,431,660 AABX/BX heterostructures. The results from the ML predictions are used to identify promising candidate heterostructures. Our study aims to accelerate the design of vdW heterostructures that have applications in spintronics, optoelectronics, and topological quantum computing.

Authors

  • Romakanta Bhattarai
    Department of Physics, Applied Physics, and Astronomy, Rensselaer Polytechnic Institute, Troy, New York 12180, United States.
  • Peter Minch
    Department of Physics, Applied Physics, and Astronomy, Rensselaer Polytechnic Institute, Troy, New York 12180, United States.
  • Trevor David Rhone
    Department of Physics, Applied Physics, and Astronomy, Rensselaer Polytechnic Institute, Troy, New York 12180, United States.

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