Data-Driven Studies of van der Waals Magnetic Heterostructures

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 AiAiiBi4Xi8/Bii4Xii6 based on a well-known heterostructure MnBi2Te4/Sb2Te3. Our study shows that combining a nonmagnetic Bii4Xii6 monolayer with a magnetic AiAiiBi4Xi8 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 AiAiiBi4Xi8/Bii4Xii6 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.