This work revises the concept of defects in crystalline solids and proposes a universal strategy for their characterization at the atomic scale using outlier detection based on statistical distances. The proposed strategy provides a generic measure that describes the distortion score of local atomic environments. This score facilitates automatic defect localization and enables a stratified description of defects, which allows to distinguish the zones with different levels of distortion within the structure. This work proposes applications for advanced materials modelling ranging from the surrogate concept for the energy per atom to the relevant information selection for evaluation of energy barriers from the mean force. Moreover, this concept can serve for design of robust interatomic machine learning potentials and high-throughput analysis of their databases. The proposed definition of defects opens up many perspectives for materials design and characterization, promoting thereby the development of novel techniques in materials science.

这项工作修改了结晶固体中缺陷的概念，并提出了一种使用基于统计距离的异常值检测在原子尺度上对其进行表征的通用策略。所提出的策略提供了描述局部原子环境失真分数的通用度量。该分数有助于自动定位缺陷并实现对缺陷的分层描述，从而可以区分结构内具有不同变形程度的区域。这项工作提出了先进材料建模的应用，从每个原子能量的替代概念到从平均力评估能垒的相关信息选择。而且，这个概念可以用于设计强大的原子间机器学习潜力和对其数据库的高通量分析。提出的缺陷定义为材料设计和表征开辟了许多视角，从而促进了材料科学新技术的发展。