Data-driven studies of magnetic two-dimensional materials

TL;DR

This paper employs a data-driven approach combining DFT and machine learning to predict and analyze the magnetic and thermodynamic properties of 2D van der Waals layered materials, aiding rapid discovery of stable magnetic 2D materials.

Contribution

It introduces a novel combined DFT and machine learning methodology for predicting magnetic properties and stability of 2D vdW materials, providing microscopic insights into magnetic ordering.

Findings

Machine learning efficiently predicts magnetic properties.

X site influences magnetic coupling significantly.

Method identifies chemically stable magnetic 2D materials.

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 A$_2$B$_2$X$_6$, based on the known material Cr$_2$Ge$_2$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.