标题： 一种预测相互作用缺陷能量的机器学习模型比较研究 显示英文标题

标题： A Comparative Study of Machine Learning Models Predicting Energetics of Interacting Defects

摘要： 相互作用的缺陷系统在现实场景中的材料中普遍存在，但从计算角度获得这些系统的原子级理解具有挑战性——这通常需要大量资源，因为需要使用超胞计算。 尽管机器学习技术在加速材料模拟方面显示出潜力，但其在涉及相互作用缺陷系统的应用仍然相对较少。 在本工作中，我们对三种不同的方法进行了比较研究，以预测含有相互作用缺陷系统的自由能变化。 我们利用来自密度泛函理论（DFT）计算的有限数据集，通过材料描述符、图神经网络和团扩张来评估模型的性能。 我们的研究结果表明，即使在有限的数据集下，团扩张模型也能实现精确的能量预测。 此外，通过从接近DFT水平的团扩张模型生成合成数据，我们获得了扩展的数据集，以评估使用图神经网络训练准确预测模型对于包含相互作用缺陷系统的数据需求。 最后提供了每种方法的计算成本的简要讨论。 这项研究为在不完美表面系统中应用机器学习技术提供了初步评估。 显示英文摘要

摘要： 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.