Ultra-high temperature ceramics melting temperature prediction via machine learning

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.