Machine learning of metal-ceramic wettability

The adhesion and wetting between metal and ceramic is a basic problem in materials science and engineering. For example, past materials selection for metal-ceramic composites has relied on random trials and heuristics due to a limited understanding of their adhesion; the large chemical/structural variability that such interfaces can have hinders the identification of the governing factors. Here based on literature data, we have developed a database with ~1, 000 experimentally measured wetting angles at different temperatures and atmospheric conditions, and come up with a model for the wettability of ionocovalent ceramics (ICs) by metals using a machine learning (ML) algorithm. The random forest model uses the testing temperature and ~40 features generated based on the chemical compositions of the metal and the ceramic as predictors and exhibits strong predictive power with an R² of ~0.86. Moreover, this model and the featurization code are integrated into a single computational pipeline to enable (1) predicting metal-IC wettability of interest and (2) high-throughput searching of ICs with the desired wettability by certain metals in the entire Inorganic Crystallographic Structure Database. As a demonstration of this pipeline, the wettability of a Li-ion and electron insulator (LEI), CaO, by molten Li is estimated and compared with ab initio molecular dynamics simulation result. This ML pipeline can serve as a practical tool for methodical design of materials in systems where certain metal-ceramic wettability is desired.