Abstract
By employing the combination of non-collinear density functional theory, artificial neural network, and classical Monte Carlo simulation we study the ferromagnetic phase transition in CrN monolayers. The artificial neural network is successfully trained from different spin alignments to reproduce interaction energy at the DFT level in two dimensions. The training is performed solely based on spin spatial angles and second-order interactions energy without any speculation for the shape of spin Hamiltonian. The neural network model predicts the angle-dependent spin energy and mimics the magnetic anisotropy energy. The neural network is then validated for a larger supercell size against density functional theory results. Finally, the trained neural network spin model is used to study the finite temperature magnetization and phase transition in the two-dimensional CrN monolayer. The Curie temperature of the CrN monolayer is estimated equal to 600 K from the temperature-dependent magnetization and specific heat.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Physica A: Statistical Mechanics and its Applications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
