Abstract
A grain boundary (GB) is a structure of great concern in materials research, which affects the mechanical properties and electrical conductivity of materials, but the microscopic thermodynamic properties of GBs cannot be explained comprehensively. In this review, we demonstrate a variety of calculation methods for GBs: density functional theory (DFT) and molecular dynamics (MDs) aim to extract the thermodynamic and kinetic properties of GBs on the atomic scale, and machine learning accelerates DFT or improves the accuracy of MDs. These methods explain the microscopic properties of a GB from different perspectives and are combined by machine learning. It is hoped that this review can inspire new ideas and provide more practical applications of computer calculations in GB engineering.
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 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.
