2023-05-11 レンセラー工科大学 (RPI)
◆これらの2次元vdW磁石は、データストレージ、スピントロニクス、そして量子コンピューティングなどの応用があります。
＜関連情報＞
- https://news.rpi.edu/content/2023/05/11/rensselaer-researcher-uses-artificial-intelligence-discover-new-materials
- https://onlinelibrary.wiley.com/doi/10.1002/adts.202300019
人工知能が導くファンデルワールス磁性体の研究 Artificial Intelligence Guided Studies of van der Waals Magnets
Trevor David Rhone, Romakanta Bhattarai, Haralambos Gavras, Bethany Lusch, Misha Salim, Marios Mattheakis, Daniel T. Larson, Yoshiharu Krockenberger, Efthimios Kaxiras
Advanced Theory and Simulations Published: 16 April 2023
DOI:https://doi.org/10.1002/adts.202300019
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.
