Researchers at Skoltech have utilized neural network-based modeling to investigate the melting points of high-entropy carbonitrides, specifically compounds combining titanium, zirconium, tantalum, hafnium, and niobium with carbon and nitrogen. By employing a DeepMD potential trained on atomic trajectories from ab initio molecular dynamics, the team accurately predicted melting temperatures and analyzed structural properties. Their findings revealed that increasing nitrogen content enhances the melting point, with the composition (TiZrTaHfNb)C₀.₇₅N₀.₂₅ achieving a maximum melting point of 3,580±30 K. These results indicate high-entropy carbonitrides’ potential as durable materials for protective coatings in extreme conditions, such as high temperatures, thermal shocks, and chemical corrosion. This innovative approach expands molecular dynamics modeling capabilities for complex multicomponent materials.
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