Ultra-high temperature ceramics melting temperature prediction via machine learning. Issue 15 (15th October 2019)

• Record Type: Journal Article
• Authors: Qu, Nan; Liu, Yong; Liao, Mingqing; Lai, Zhonghong; Zhou, Fei; Cui, Puchang; Han, Tianyi; Yang, Danni; Zhu, Jingchuan
• Is Part Of: Ceramics international. Volume 45:Issue 15(2019)
• Page Start: 18551
• Page End: 18555
• Publication Date: 2019-10-15
• Subjects: Ultra-high temperature ceramics -- Machine learning -- Melting temperature prediction; Ceramics -- Periodicals; Céramique industrielle -- Périodiques; Ceramics; Periodicals; Electronic journals; 666
• DOI: 10.1016/j.ceramint.2019.06.076
• Languages: English
• ISSNs: 0272-8842

Abstract

Abstract: Melting temperature has great influence on the high temperature properties and working temperature limits of ultra-high temperature ceramics (UHTCs) In order to bypass the challenge in the measurement of ultra-high melting points, this paper proposed a novel method to predict UHTCs melting temperature via machine learning. A dataset including more than ten thousand melting temperature data has been established, which covers 8 elements and most of the known non-oxide UHTCs. We built up an element to ceramic system framework by back propagation artificial neural network (BPANN) with the accuracy approaching to 90% and the correlation coefficients approaching to 0.95. Our work provides a probability to get the high accuracy melting temperature of UHTCs, and a more convenient way to develop novel materials with higher working temperature. The given case of melting temperature prediction of Hf-C-N ceramics proves the generality of the artificial neural network (ANN). An inter-validation of melting temperature prediction using our network with materials thermodynamics and density functional theory (DFT) has been demonstrated, indicating that our network is of powerful prediction ability.