Protocol to predict mechanical properties of multi-element ceramics using machine learning

• Published in: STAR protocols Vol. 3; no. 3; p. 101552
• Main Authors: Tang, Yunqing, Zhang, Dong, Liu, Ruiliang, Li, Dongyang
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 16.09.2022; Elsevier
• Subjects: Computer sciences; Material sciences; Physics; Protocol; Material sciences; Computer sciences; Physics
• ISSN: 2666-1667; 2666-1667
• DOI: 10.1016/j.xpro.2022.101552

Identifying and designing high-performance multi-element ceramics based on trial-and-error approaches are ineffective and expensive. Here, we present a machine-learning-accelerated method for prediction of mechanical properties of multi-element ceramics, based on the density functional theory calculation database. Specific bonding characteristics are used as highly efficient machine learning descriptors. This protocol describes a low-cost, high-efficiency, and reliable workflow for developing advanced ceramics with superior mechanical properties. For complete details on the use and execution of this protocol, please refer to Tang et al. (2021). [Display omitted] •Accelerate design of high-mechanical-performance ceramics through machine learning•Predict mechanical properties of ceramics using low dimensional descriptors•Obtain mechanical properties of multi-element ceramics from simple ceramics•This protocol is applicable for multiple rock-salt ceramics and WC-type carbides Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Identifying and designing high-performance multi-element ceramics based on trial-and-error approaches are ineffective and expensive. Here, we present a machine-learning-accelerated method for prediction of mechanical properties of multi-element ceramics, based on the density functional theory calculation database. Specific bonding characteristics are used as highly efficient machine learning descriptors. This protocol describes a low-cost, high-efficiency, and reliable workflow for developing advanced ceramics with superior mechanical properties.