We developed a method for evaluating the accuracies of the local properties of DFT functionals in detail using a clustering method based on machine learning and structural/electronic descriptors. We generated 36 clusters consistent with human intuition using 30,436 carbon atoms from the QM9 dataset. The results were used to evaluate 13C NMR chemical shifts calculated using 84 DFT functionals. Carbon atoms were grouped based on their similar environments, reducing errors within these groups. This enables more accurate assessment of the accuracy using a specific DFT functional. Therefore, the present atomic clustering provides more detailed insight into accuracy verification.

中文翻译：

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使用基于考虑周围环境的结构和电子描述符的原子聚类来评估 DFT 泛函的局部属性

我们开发了一种使用基于机器学习和结构/电子描述符的聚类方法详细评估 DFT 泛函局部属性准确性的方法。我们使用 QM9 数据集中的 30,436 个碳原子生成了 36 个与人类直觉一致的簇。结果用于评估13使用 84 个 DFT 泛函计算的 13 C NMR 化学位移。碳原子根据其相似的环境进行分组，减少了这些组内的错误。这样可以使用特定的 DFT 函数更准确地评估准确性。因此，当前的原子聚类为准确性验证提供了更详细的见解。