Artificial Intelligence Guided Studies of van der Waals Magnets
Artificial Intelligence Guided Studies of van der Waals Magnets
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DOI：
10.1002/adts.202300019
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发表时间：
2023-04
影响因子：
3.3
通讯作者：
T. Rhone;R. Bhattarai;Haralambos Gavras;Bethany Lusch;Misha Salim;M. Mattheakis;Daniel T. Larson;Y. Krockenberger;E. Kaxiras
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文献类型：
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作者：
T. Rhone;R. Bhattarai;Haralambos Gavras;Bethany Lusch;Misha Salim;M. Mattheakis;Daniel T. Larson;Y. Krockenberger;E. Kaxiras
建立了一个探索大量候选范德华(VDW)材料的材料信息学框架。特别是，在这项研究中，通过结合高通量密度泛函理论计算和人工智能(AI)来研究大空间的单层过渡金属卤化物，以加速发现稳定的材料和预测其磁性。生成能被用作化学稳定性的代用指标。半监督学习被用来缓解稀疏标记材料数据的挑战，以提高人工智能模型的性能。这种方法利用人工智能识别数据中的模式、从数据中学习材料的数学表示和预测材料性能的能力，为快速发现化学稳定的VDW磁体创造了途径。利用这种方法，发现了在数据存储和自旋电子学中具有潜在应用的以前未被探索的VDW磁性材料。
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.