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,hardwareaccelerated,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.
基金
The GPU computing nodes used in this study were procured through the research funding from the Department of Science and Technology(DST),Government of India,under grant number DST/TMD/MES/2K18/28.
