The temperature-dependent behavior of defect densities within a crystalline structure is intricately linked to the phenomenon of vibrational entropy. Traditional methods for evaluating vibrational entropy are computationally intensive, limiting their practical utility. We show that total entropy can be decomposed into atomic site contributions and rigorously estimate the locality of site entropy. This analysis suggests that vibrational entropy can be effectively predicted using a surrogate model for site entropy. We employ machine learning to develop such a surrogate models employing the Atomic Cluster Expansion model. We supplement our rigorous analysis with an empirical convergence study. In addition we demonstrate the performance of our method for predicting vibrational formation entropy and attempt frequency of the transition rates, on point defects such as vacancies and interstitials.

翻译：晶体结构中缺陷密度的温度依赖性行为与振动熵现象密切相关。传统评估振动熵的方法计算强度大，限制了其实际应用。我们证明总熵可分解为原子位点贡献，并严格估计了位点熵的局域性。这一分析表明，振动熵可通过位点熵的代理模型有效预测。我们采用机器学习方法，利用原子团簇展开模型开发此类代理模型。在严格分析基础上，我们补充了经验收敛性研究。此外，我们展示了该方法在预测点缺陷（如空位和间隙原子）的振动形成熵及跃迁速率尝试频率方面的性能。