Discovering melting temperature prediction models of inorganic solids by combining supervised and unsupervised learning
Discovering melting temperature prediction models of inorganic solids by combining supervised and unsupervised learning
The melting temperature is important for materials design because of its relationship with thermal stability, synthesis, and processing conditions. Current empirical and computational melting point estimation techniques are limited in scope, computational feasibility, or interpretability. We report the development of a machine learning methodology for predicting melting temperatures of binary …