High-throughput discovery of high Curie point two-dimensional ferromagnetic materials

被引:93
作者
Kabiraj, Arnab [1 ]
Kumar, Mayank [1 ]
Mahapatra, Santanu [1 ]
机构
[1] Indian Inst Sci IISc Bangalore, Dept Elect Syst Engn, Nanoscale Device Res Lab, Bangalore 560012, Karnataka, India
关键词
DYNAMICS; CRYSTAL; METALS;
D O I
10.1038/s41524-020-0300-2
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Databases for two-dimensional materials host numerous ferromagnetic materials without the vital information of Curie temperature since its calculation involves a manually intensive complex process. In this work, we develop a fully automated, hardware-accelerated, dynamic-translation based computer code, which performs first principles-based computations followed by Heisenberg model-based Monte Carlo simulations to estimate the Curie temperature from the crystal structure. We employ this code to conduct a high-throughput scan of 786 materials from a database to discover 26 materials with a Curie point beyond 400 K. For rapid data mining, we further use these results to develop an end-to-end machine learning model with generalized chemical features through an exhaustive search of the model space as well as the hyperparameters. We discover a few more high Curie point materials from different sources using this data-driven model. Such material informatics, which agrees well with recent experiments, is expected to foster practical applications of two-dimensional magnetism.
引用
收藏
页数:9
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