Abstract
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.
Highlights
The recent experimental demonstration of ferromagnetism in twodimensional (2D) materials: CrI31 and Cr2Ge2Te62 at low temperatures, has opened a new horizon of nanotechnology research since these materials inherit the potential to revolutionize engineering fields like spintronics[3], valleytronics[4], sensing and memory technologies[5]
We develop a code, which performs first principles-based computations followed by Heisenberg model-based Monte Carlo simulations to predict the Curie point accurately from any magnetic 2D material crystal structure
Eliminating the heuristics-based approach, pymatgen helps to automate the process of the temperature orCurie point (TC) calculation, and makes it more rigorous and reliable [see Methods]
Summary
The recent experimental demonstration of ferromagnetism in twodimensional (2D) materials: CrI31 and Cr2Ge2Te62 at low temperatures, has opened a new horizon of nanotechnology research since these materials inherit the potential to revolutionize engineering fields like spintronics[3], valleytronics[4], sensing and memory technologies[5] In their classical work, Mermin and Wanger[6] showed that under an isotropic Heisenberg model, long-range magnetic order must be absent in 2D. We develop a code, which performs first principles-based computations followed by Heisenberg model-based Monte Carlo simulations to predict the Curie point accurately from any magnetic 2D material crystal structure Software engineering on this code makes it capable to execute such rigorous calculations in a high-throughput manner, even on a workstation-grade computer with GPU (graphical processing unit) acceleration. Our work significantly upgrades the computational materials toolbox to foster practical applications of two-dimensional magnetism
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