Recently high entropy alloys (HEA) have shown remarkable potential due to their extraordinary properties and applications. HEAs are explored to create a new class of materials with an attractive set of properties that are difficult to achieve by conventional materials. Short-range ordering (SRO) is important in determining various materials properties at nanometer scale, such as phase stability. The relationship between SRO and phase stability can be understood through the enthalpy of mixing. Cluster expansion (CE) is often used to understand the relationship between the enthalpy of mixing and SRO parameters. Although exact, CE must be truncated in practice beyond some maximal-sized cluster, leading to truncation errors. In this work, as an alternative, a neural network is trained to understand the relationship between SRO and enthalpy of mixing among the various binary subsystems of NbTiVZr HEA. For training, a large pool of structures and their corresponding correlation functions (or SRO parameters) are generated using the alloy theoretic automated toolkit (ATAT) software for each subsystem. First-principles calculations are used to determine the enthalpy of mixing of these structures. This database is used to train a neural network and the predicted values of enthalpy of mixing from the trained neural network are found to be reasonably accurate and better than the corresponding CE model. The neural network approach is found to clarify the complex relationship between the enthalpy of mixing and SRO, especially when there is a limitation over the number of fitting parameters due to smaller size of databases.

最近，高熵合金（HEA）由于其非凡的性能和应用而显示出巨大的潜力。HEA 旨在创造一种新型材料，具有传统材料难以实现的一系列有吸引力的特性。短程有序 (SRO) 对于确定纳米尺度的各种材料特性（例如相稳定性）非常重要。SRO和相稳定性之间的关系可以通过混合焓来理解。簇膨胀 (CE) 通常用于了解混合焓与 SRO 参数之间的关系。尽管是精确的，但在实践中，CE 必须被截断超出某个最大大小的簇，从而导致截断错误。在这项工作中，作为替代方案，NbTiVZr HEA。对于训练，使用每个子系统的合金理论自动化工具包 (ATAT) 软件生成大量结构及其相应的相关函数（或 SRO 参数）。第一原理计算用于确定这些结构的混合焓。该数据库用于训练神经网络，并且发现训练的神经网络的混合焓预测值相当准确，并且优于相应的 CE 模型。神经网络方法可以阐明混合焓和 SRO 之间的复杂关系，特别是当由于数据库规模较小而导致拟合参数数量受到限制时。