Abstract

Although high-entropy ceramics (HECs) are greatly attractive because of their superior properties over conventional ceramics, there is a lack of reliable and effective design guidelines for producing HECs with the wished-for mechanical properties. The often-used trial-and-error testing approach or case-by-case calculations without clear design guidelines are ineffective and expensive. Here, we propose a machine-learning accelerated strategy to design HECs with the desired mechanical properties. Using rock-salt ceramics as representative examples, we demonstrate that their mechanical properties are determined synergistically by different types of bonds, and bond properties of multi-element ceramics can be weighted from those of the involved constituents. Machine-learning models are developed to describe the correlations between bond characteristics and macro-mechanical properties, which show good prediction accuracy, as verified by computational and experimental data. The strategy for the HEC design, developed based on bond-mechanical property correlations and machine-learning methodology, provides a low-cost, highly efficient, and reliable method for developing advanced ceramics with superior mechanical properties. A bond-parameter-guided strategy is proposed for designing advanced ceramics Alloying elements are classified for tailoring mechanical properties of ceramics Bond parameters are effective descriptors for mechanical properties of ceramics Bond-based machine-learning predictions are effective for high-entropy ceramics Tang et al. reveal the correlations between bond parameters and mechanical properties of ceramics, and bonding characteristics-related descriptors are developed to evaluate mechanical properties of high-entropy ceramics. The incorporation of a machine-learning methodology and effective descriptors provides a low-cost, highly efficient, and reliable method for developing advanced ceramics with superior mechanical properties

Full Text
Published version (Free)

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 call