Baidyshev Viktor S, Tantardini Christian, Kvashnin Alexander G
Project Center for Energy Transition and ESG, Skolkovo Institute of Science and Technology, Bolshoi Blv. 30, Building 1, Moscow, 121205, Russian Federation.
Hylleraas Center, Department of Chemistry, UiT The Arctic University of Norway, PO Box 6050, Langnes, Tromsö, 9037, Norway.
Sci Rep. 2024 Nov 19;14(1):28678. doi: 10.1038/s41598-024-78377-4.
The melting temperature is a crucial property of materials that determines their potential applications in different industrial fields. In this study, we used a deep neural network potential to describe the structure of high-entropy (TiZrTaHfNb)CN carbonitrides (HECN) in both solid and liquid states. This approach allows us to predict heating and cooling temperatures depending on the nitrogen content to determine the melting temperature and analyze structure changes from atomistic point of view. A steady increase in nitrogen content leads to increasing melting temperature, with a maximum approaching for 25% of nitrogen in the HECN. A careful analysis of pair correlations, together with calculations of entropy in the considered liquid phases of HECNs allows us to explain the origin of the nonlinear enhancement of the melting temperature with increasing nitrogen content. The maximum melting temperature of 3580 ± 30 K belongs to (TiZrTaHfNb)CN composition. The improved melting behavior of high-entropy compounds by the addition of nitrogen provides a promising way towards modification of thermal properties of functional and constructional materials.
熔点是材料的关键特性，它决定了材料在不同工业领域的潜在应用。在本研究中，我们使用深度神经网络势来描述高熵（TiZrTaHfNb）CN碳氮化物（HECN）在固态和液态下的结构。这种方法使我们能够根据氮含量预测加热和冷却温度，以确定熔点，并从原子角度分析结构变化。氮含量的稳步增加会导致熔点升高，在HECN中，当氮含量达到25%时接近最大值。对成对相关性的仔细分析，以及对HECNs所考虑液相中的熵的计算，使我们能够解释随着氮含量增加熔点非线性增强的原因。最高熔点为3580±30K，属于（TiZrTaHfNb）CN组成。通过添加氮来改善高熵化合物的熔化行为，为功能材料和结构材料热性能的改性提供了一条有前景的途径。
