2023/12/07 by Haichang Lu, Tai Yang, Lu, Haichang +7
Materials Science · #2D Materials and Applications #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Machine Learning in Materials Science #Magnetic and transport properties of perovskites and related materials #Materials Science (cond-mat.mtrl-sci)
paper · pdf · doi:10.48550/arxiv.2312.04497
openalex publication_date 2023/12/07 · openalex created_date 2023/12/09 · openalex updated_date 2026/07/28
Theoretical prediction of the 2nd-order magnetic transition temperature (TM) used to be arduous. Here, we develop a first principle-based, fully automatic structure-to-TM method for two-dimensional (2D) magnets whose effective Hamiltonians follow the Heisenberg model. The Heisenberg exchanges, which can be calculated to an arbitrary shell, are transferred into the Monte Carlo calculation. Using Cr-based magnets as the showcases, we show that our method is a powerful tool to study the 2D magnets in two aspects. First, considering long-range exchanges enables us to identify the spin frustration in the suspended CrTe2 monolayer, whereas the heterostructure calculations reveal that the ferromagnetism can be recovered if the monolayer CrTe2 is grown onto various 2D substrates. Second, we realize a high-throughput screening of novel magnets discovered by random structure searches. Six 2D Cr chalcogenides are selected to have high TM. Our work provides a new insight for the study of 2D magnets and helps accelerate the pace of magnetic materials data-mining.