The empirical rules for the prediction of solid solution formation proposed so far in the literature usually have very compromised predictability. Some rules with seemingly good predictability were, however, tested using small data sets. Based on an unprecedented large dataset containing 1252 multicomponent alloys, machine-learning methods showed that the formation of solid solutions can be very accurately predicted (93%). The machine-learning results help identify the most important features, such as molar volume, bulk modulus, and melting temperature. As such a new thermodynamics-based rule was developed to predict solid–solution alloys. The new rule is nonetheless slightly less accurate (73%) but has roots in the physical nature of the problem. The new rule is employed to predict solid solutions existing in the three blocks, each of which consists of 9 elements. The predictions encompass face-centered cubic (FCC), body-centered cubic (BCC), and hexagonal closest packed (HCP) structures in a high throughput manner. The validity of the prediction is further confirmed by CALculations of PHAse Diagram (CALPHAD) calculations with high consistency (94%). Since the new thermodynamics-based rule employs only elemental properties, applicability in screening for solid solution high-entropy alloys is straightforward and efficient.

迄今为止，文献中提出的用于预测固溶体形成的经验规则通常具有非常不利的可预测性。但是，使用小的数据集测试了一些看似可预测性良好的规则。基于包含1252种多组分合金的前所未有的大型数据集，机器学习方法显示可以非常准确地预测固溶体的形成（93％）。机器学习结果有助于确定最重要的特征，例如摩尔体积，体积模量和熔融温度。因此，开发了一种基于热力学的新规则来预测固溶合金。尽管如此，新规则的准确性稍差（73％），但其根源在于问题的物理性质。运用新规则来预测存在于这三个区块中的可靠解决方案，每个元素由9个元素组成。这些预测以高通量方式包含面心立方（FCC），体心立方（BCC）和六边形最紧密堆积（HCP）结构。通过具有高一致性（94％）的PHAse图的计算（CALPHAD）计算进一步确认了预测的有效性。由于新的基于热力学的规则仅采用元素性质，因此在筛查固溶高熵合金中的适用性简单有效。