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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.

 

In this work, we investigate magnetic monolayers of the form A i A ii B 4 X 8 based on the well-known intrinsic topological magnetic van der Waals (vdW) material MnBi 2 Te 4 (MBT) using first-principles calculations and machine learning techniques. We select an initial subset of structures to calculate the thermodynamic properties, electronic properties, such as the band gap, and magnetic properties, such as the magnetic moment and magnetic order using density functional theory (DFT). Data analytics approaches are used to gain insight into the microscopic origin of materials’ properties. The dependence of materials’ properties on chemical composition is also explored. For example, we find that the formation energy and magnetic moment depend largely on A and B sites whereas the band gap depends on all three sites. Finally, we employ machine learning tools to accelerate the search for novel vdW magnets in the MBT family with optimized properties. This study creates avenues for rapidly predicting novel materials with desirable properties that could enable applications in spintronics, optoelectronics, and quantum computing.