The representation of atomic configurations through cluster correlations, along with the cluster expansion approach, has long been used to predict formation energies and determine the thermodynamic stability of alloys. In this work, a comparison is conducted between the traditional cluster expansion method based on density functional theory and other potential machine learning models, including decision tree‐based ensembles and multi‐layer perceptron regression, to explore the alloying behavior of different elements in multi‐component alloys. Specifically, these models are applied to investigate the thermodynamic stability of triple transition‐metal MXenes, a multi‐component alloy in the largest family of 2D materials that are gaining attention for several outstanding properties. The findings reveal the triple transition‐metal ground‐state configurations in this system and demonstrate how the configuration of transition metal atoms (Ti, Mo, and V atoms) influences the formation energy of this alloy. Moreover, the performance of machine learning algorithms in predicting formation energies and identifying ground‐state structures is thoroughly discussed from various aspects.

中文翻译：

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通过基于聚类相关的机器学习预测三重过渡金属 (Ti−Mo−V)3C2${\rm (Ti-Mo-V)}_3{\rm C}_2$ MXene 的热力学稳定性

通过簇相关性表示原子构型以及簇扩展方法，长期以来一直用于预测形成能并确定合金的热力学稳定性。在这项工作中，对基于密度泛函理论的传统聚类扩展方法和其他潜在的机器学习模型（包括基于决策树的集成和多层感知器回归）进行了比较，以探索多元合金中不同元素的合金化行为。成分合金。具体来说，这些模型用于研究三重过渡金属 MXene 的热力学稳定性，这是最大的二维材料家族中的一种多组分合金，因其多种出色的性能而受到关注。研究结果揭示了该体系中的三重过渡金属基态构型，并证明了过渡金属原子（Ti、Mo 和 V 原子）的构型如何影响该合金的形成能。此外，还从各个方面深入讨论了机器学习算法在预测地层能和识别基态结构方面的性能。