Abstract
Abstract Recently, Fe-based superconductors have shown promising properties of high critical temperature and high upper critical fields, which are prerequisites for applications in high-field magnets. Critical temperature, T c, is an important characteristic correlated with crystallographic and electronic structures. By doping with foreign ions in the crystal structure, T c can be modified, which however requires significant manpower and resources for materials synthesis and characterizations. In this study, we develop the Gaussian process regression model to predict T c of doped Fe-based superconductors based on structural and topological parameters, including the lattice constants, volume, and bonding parameter topological index H 31. The model is stable and accurate, contributing to fast T c estimations.
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 callDisclaimer: 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.
