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A materials informatics framework to explore a large number of candidate van der Waals (vdW) materials is developed. In particular, in this study a large space of monolayer transition metal halides is investigated by combining high‐throughput density functional theory calculations and artificial intelligence (AI) to accelerate the discovery of stable materials and the prediction of their magnetic properties. The formation energy is used as a proxy for chemical stability. Semi‐supervised learning is harnessed to mitigate the challenges of sparsely labeled materials data in order to improve the performance of AI models. This approach creates avenues for the rapid discovery of chemically stable vdW magnets by leveraging the ability of AI to recognize patterns in data, to learn mathematical representations of materials from data and to predict materials properties. Using this approach, previously unexplored vdW magnetic materials with potential applications in data storage and spintronics are identified.

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$$\hbox {A}_2\hbox {B}_2\hbox {X}_6$$${\text{A}}_2{\text{B}}_2{\text{X}}_6$, based on the known material$$\hbox {Cr}_2\hbox {Ge}_2\hbox {Te}_6$$${\text{Cr}}_2{\text{Ge}}_2{\text{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.