Cluster expansion (CE) has gained an increasing level of popularity in recent years, and its applications go far beyond its original root in binary alloys, reaching even complex crystalline systems often used in energy materials research. Similar to other modern machine learning approaches in materials science, many strategies have been proposed for training and fitting the CE models to first-principles calculation results. Here, we propose a new strategy for constructing a training set based on their relevance in Monte Carlo sampling for statistical analysis and reduction of the expected error. The CE model constructed from the proposed approach has lower dependence on the specific details of the training set, thereby increasing the reproducibility of the model. The same method can be applied to other machine learning approaches where it is desirable to sample relevant configurational space with a small set of training data, which is often the case when they consist of first-principles calculations.

近年来，簇扩展 (CE) 越来越受欢迎，其应用远远超出其最初的二元合金根源，甚至涉及能源材料研究中经常使用的复杂晶体系统。与材料科学中的其他现代机器学习方法类似，已经提出了许多策略来训练 CE 模型并将其拟合到第一性原理计算结果。在这里，我们提出了一种新的策略，用于基于它们在蒙特卡罗采样中的相关性来构建训练集，以进行统计分析和减少预期误差。由所提出的方法构建的CE模型对训练集的具体细节的依赖性较低，从而增加了模型的可重复性。