Data-driven studies of topological magnetic vdW materials

First-principles calculations and machine learning techniques are used to investigate monolayers of the form AB₂X₄ based on the well-known intrinsic topological magnetic vdW material MnBi₂Te₄. In this study, we consider a very large number of candidate materials (~10⁴) formed by tuning the chemical composition of AB₂X₄. Investigating this enormous number of candidates by first-principles calculations or experiments is prohibitive. The use of machine learning is a promising way to efficiently explore the entire chemical space thereby accelerating materials discovery. We select an initial subset of 240 structures for investigation using density functional theory (DFT). We calculate the thermodynamic properties, electronic properties, such as the band gap, and magnetic properties, such as the magnetic moment, magnetic order, and the exchange energy. Next, we train a machine learning model to successfully make predictions of various properties that will be useful to accelerate the exploration of the entire chemical space. Our analysis shows that the formation energy and the magnetic moment of the system depend largely on A and B sites, whereas the bandgap depends on all three sites. This study creates avenues for discovering novel materials with desirable properties that are crucial for spintronics, optoelectronics, quantum computing, and quantum communication.