Interacting defect systems are ubiquitous in materials under realistic scenarios, yet gaining an atomic-level understanding of these systems from a computational perspective is challenging - it often demands substantial resources due to the necessity of employing supercell calculations. While machine learning techniques have shown potential in accelerating materials simulations, their application to systems involving interacting defects remains relatively rare. In this work, we present a comparative study of three different methods to predict the free energy change of systems with interacting defects. We leveraging a limited dataset from Density Functional Theory(DFT) calculations to assess the performance models using materials descriptors, graph neural networks and cluster expansion. Our findings indicate that the cluster expansion model can achieve precise energetics predictions even with this limited dataset. Furthermore, with synthetic data generate from cluster expansion model at near-DFT levels, we obtained enlarged dataset to assess the demands on data for training accurate prediction models using graph neural networks for systems featuring interacting defects. A brief discussion of the computational cost for each method is provided at the end. This research provide a preliminary evaluation of applying machine learning techniques in imperfect surface systems.

对涉及相互作用缺陷的系统的自由能变化进行预测的三种不同方法的比较研究表明，簇展开模型可以在有限的数据集上精确预测能量学，同时使用簇展开模型生成的近似密度泛函理论的合成数据集对使用图神经网络训练准确预测模型的数据需求进行了评估，为在不完美的表面系统中应用机器学习技术提供了初步评估。

相互作用缺陷能量预测的机器学习模型比较研究