Predictive modelling for contact angle of liquid metals and oxide ceramics by comparing Gaussian process regression with other machine learning methods. Issue 1 (1st January 2022)

• Record Type: Journal Article
• Authors: Jiang, Dewen; Wang, Zhenyang; Zhang, Jianliang; Jiang, Dejun; Liu, Fulong; Hao, Liangyuan
• Is Part Of: Ceramics international. Volume 48:Issue 1(2022)
• Page Start: 665
• Page End: 673
• Publication Date: 2022-01-01
• Subjects: Machine learning -- Contact angle -- Oxide ceramics -- Liquid metal -- Data-driven; Ceramics -- Periodicals; Céramique industrielle -- Périodiques; Ceramics; Periodicals; Electronic journals; 666
• DOI: 10.1016/j.ceramint.2021.09.146
• Languages: English
• ISSNs: 0272-8842

Abstract

Abstract: Wettability has a major effect on the performance of the corrosion of ceramic refractory under normal operating conditions. Contact angle measurement is available to characterize the wettability of liquid metals and oxide ceramics. Therefore, it is necessary to develop a contact angle prediction model with generalizability. This work emphasizes on developing a model for predicting the contact angle of a liquid metal with a solid oxide and analyzes the influence of factors affecting the contact angle when contact angle is predicted. In this paper, six contact angle prediction models are developed based on machine learning methods and contact angle data from the previous literature. The comparison between six contact angle prediction models evidences that the gaussian process regression (GPR) model has the best prediction accuracy and reaching 96%. Furthermore, the comparative results indicate that when surface energy of metal, surface energy of oxide, formation free energy of oxide, and bandgap energy of oxide are ignored respectively, the prediction accuracy of the model decreases by 4%, 3%, 1% and 1% respectively.