Although high-entropy materials are attracting considerable interest due to a combination of useful properties and promising applications, predicting their formation remains a hindrance for rational discovery of new systems. Experimental approaches are based on physical intuition and/or expensive trial and error strategies. Most computational methods rely on the availability of sufficient experimental data and computational power. Machine learning (ML) applied to materials science can accelerate development and reduce costs. In this study, we propose an ML method, leveraging thermodynamic and compositional attributes of a given material for predicting the synthesizability (i.e., entropy-forming ability) of disordered metal carbides. The relative importance of the thermodynamic and compositional features for the predictions are then explored. The approach’s suitability is demonstrated by comparing values calculated with density functional theory to ML predictions. Finally, the model is employed to predict the entropy-forming ability of 70 new compositions; several predictions are validated by additional density functional theory calculations and experimental synthesis, corroborating the effectiveness in exploring vast compositional spaces in a high-throughput manner. Importantly, seven compositions are selected specifically, because they contain all three of the Group VI elements (Cr, Mo, and W), which do not form room temperature-stable rock-salt monocarbides. Incorporating the Group VI elements into the rock-salt structure provides further opportunity for tuning the electronic structure and potentially material performance.

尽管由于有用特性和有前途的应用的结合，高熵材料引起了极大的兴趣，但预测它们的形成仍然是合理发现新系统的障碍。实验方法是基于物理直觉和/或昂贵的反复试验策略。大多数计算方法依赖于足够的实验数据和计算能力的可用性。应用于材料科学的机器学习（ML）可以加速开发并降低成本。在这项研究中，我们提出了一种ML方法，该方法利用给定材料的热力学和成分属性来预测无序金属碳化物的合成能力（即，熵形成能力）。然后探讨了热力学和成分特征对预测的相对重要性。通过将用密度泛函理论计算的值与ML预测值进行比较，证明了该方法的适用性。最后，该模型用于预测70种新成分的熵形成能力。额外的密度泛函理论计算和实验综合验证了几种预测，从而证实了以高通量方式探索巨大的组成空间的有效性。重要的是，要特别选择七种成分，因为它们包含所有三族VI元素（Cr，Mo和W），它们不会形成室温稳定的岩盐一碳化物。将VI族元素掺入岩石盐结构中提供了进一步的机会来调整电子结构和潜在的材料性能。