Data-driven studies of magnetic two-dimensional materials

Trevor David Rhone,Amir Yacoby,Shaan Desai,Steven B Torrisi,Daniel T Larson,Wei Chen,Efthimios Kaxiras

doi:10.1038/s41598-020-72811-z

Trevor David Rhone, Amir Yacoby + Show 5 more

Open Access

https://doi.org/10.1038/s41598-020-72811-z

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Journal: Scientific Reports	Publication Date: Sep 25, 2020
Citations: 42	License type: open-access

Affiliation: Harvard University

Abstract

We use a data-driven approach to study the magnetic and thermodynamic properties of van der Waals (vdW) layered materials. We investigate monolayers of the form hbox {A}_2hbox {B}_2hbox {X}_6, based on the known material hbox {Cr}_2hbox {Ge}_2hbox {Te}_6, using density functional theory (DFT) calculations and machine learning methods to determine their magnetic properties, such as magnetic order and magnetic moment. We also examine formation energies and use them as a proxy for chemical stability. We show that machine learning tools, combined with DFT calculations, can provide a computationally efficient means to predict properties of such two-dimensional (2D) magnetic materials. Our data analytics approach provides insights into the microscopic origins of magnetic ordering in these systems. For instance, we find that the X site strongly affects the magnetic coupling between neighboring A sites, which drives the magnetic ordering. Our approach opens new ways for rapid discovery of chemically stable vdW materials that exhibit magnetic behavior.

Highlights

We use a data-driven approach to study the magnetic and thermodynamic properties of van der Waals layered materials
Long-range ferromagnetism and anti-ferromagnetism in 2D crystals has recently been d iscovered[20,21,22,23,24], sparking a push to understand the properties of these 2D magnetic materials and to discover new ones with improved behavior5,19,25–32. 2D crystals provide a unique platform for exploring the microscopic origins of magnetic ordering in reduced dimensions
In order to develop a path towards discovering 2D magnetic materials, we generate a database of structures based on monolayer Cr2Ge2Te6 (Fig. 1a) using density functional theory (DFT) calculations with non-collinear spin and spin-orbit interactions included

Summary

Introduction

We use a data-driven approach to study the magnetic and thermodynamic properties of van der Waals (vdW) layered materials. Our approach opens new ways for rapid discovery of chemically stable vdW materials that exhibit magnetic behavior. Long-range magnetic order is strongly suppressed in 2D according to the MerminWagner theorem[33], but magnetocrystalline anisotropy can stabilize magnetic ordering[34] This magnetic anisotropy is driven by spin-orbit coupling which depends on the relative positions of atoms and their identities. To calculate the formation energy, we obtain the total energies of systems at zero temperature, and obtain the difference in total energy between the crystal and its constituent elements in their respective crystal phases This quantity determines whether the structure is thermodynamically stable or would decompose. By computing the phonon spectrum of the 0K structure, the presence of negative phonon frequencies

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Conclusion