Data-driven study of magnetic interactions of transition-metal based 2D materials

POSTER

Abstract

Engineering magnetic interactions are critical to control magnetic behavior for device applications. Depending on the presence or absence of inversion symmetries, the magnetic interactions stabilize several exciting magnetic behaviors such as formation of chiral helimagnets, skyrmions, and even quantum spin liquid. Various exchange interactions and magnetic anisotropy within crystal lattices can be estimated using DFT-based simulations approaches, based on which an effective spin only Hamiltonian can be constructed. This multi-scale modeling allows one to predict magnetic properties for real materials. Here, we propose to use a data-driven approach to screen for suitable candidates exhibiting magnetic skyrmions, followed by evaluating their exchange interactions in transition-metal based 2D magnetic materials. This curated dataset of 2D magnetic materials and computed magnetic interactions can then be used to develop a combined protocol to search for similar compounds in a variety of materials space followed by constructing predictive machine learning models to understand magnetic behavior of such systems.

*This work was carried out under the auspices of the U.S. DOE NNSA under Contract No. 89233218CNA000001, and was supported by the LANL LDRD Program.

Presenters

  • Ayana Ghosh

    • Univ of Connecticut - Storrs
    • Materials Science and Engineering, University of Connecticut
    • University of Connecticut

Authors

  • Ayana Ghosh

    • Univ of Connecticut - Storrs
    • Materials Science and Engineering, University of Connecticut
    • University of Connecticut

  • Shizeng Lin

    • Theoretical Division, Los Alamos National Laboratory
    • Los Alamos Natl Lab
    • Theoretical Division, T-4, Los Alamos National Laboratory
    • Los Alamos National Laboratory
    • Theoretical Division, Los Alamos National Lab
    • Theoretical Division, T-4 and CNLS, Los Alamos National Laboratory
    • Los Alamos National Lab

  • Jian-Xin Zhu

    • Los Alamos National Laboratory
    • Los Alamos National Lab
    • Los Alamos Natl Lab
    • Theoretical Division, Los Alamos National Laboratory