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Abstract Van der Waals (vdW) materials are an indispensable part of functional device technology due to their versatile physical properties and ease of exfoliating to the low‐dimensional limit. Among all the compounds investigated so far, the search for magnetic vdW materials has intensified in recent years, fueled by the realization of magnetism in 2D. However, metallic magnetic vdW systems are still uncommon. In addition, they rarely host high‐mobility charge carriers, which is an essential requirement for high‐speed electronic applications. Another shortcoming of 2D magnets is that they are highly air sensitive. Using chemical reasoning, TaCo2Te2is introduced as an air‐stable, high‐mobility, magnetic vdW material. It has a layered structure, which consists of Peierls distorted Co chains and a large vdW gap between the layers. It is found that the bulk crystals can be easily exfoliated and the obtained thin flakes are robust to ambient conditions after 4 months of monitoring using an optical microscope. Signatures of canted antiferromagntic behavior are also observed at low‐temperature. TaCo2Te2shows a metallic character and a large, nonsaturating, anisotropic magnetoresistance. Furthermore, the Hall data and quantum oscillation measurements reveal the presence of both electron‐ and hole‐type carriers and their high mobility.
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Artificial Intelligence Guided Studies of van der Waals Magnets
Abstract 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.
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- Award ID(s):
- 2044842
- PAR ID:
- 10407156
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Theory and Simulations
- Volume:
- 6
- Issue:
- 6
- ISSN:
- 2513-0390
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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