FaVAD：用于晶体结构缺陷表征和可视化的软件工作流程
FaVAD: A software workflow for characterisation and visualizing of defects in crystalline structures
摘要：
晶体材料中缺陷和缺陷动力学的分析对于基础科学和广泛的应用工程是重要的。随着系统规模的增加，对分子动力学模拟数据的分析变得重要。在这里，我们提出了一个半自动识别和分类晶体结构中的缺陷的工作流程，结合了一种新的方法与几个已经存在的开源软件包的缺陷描述。我们的方法解决了通常相对较小的体积分数的样品，热运动和潜在的不可预见的原子配置（缺陷类型）照射后的存在所带来的关键挑战。任何原子的局部环境被转换成旋转不变的描述向量（“指纹”），其可以与已知的缺陷类型进行比较，并且还产生适合于分类的距离度量。不能与已知结构相关联的矢量指示新类型的缺陷。作为概念验证，我们将我们的方法应用于铁样品上，以分析由10 keV初级撞击原子引起的碰撞级联所引起的缺陷。所得结果与文献报道值吻合良好。
英文摘要：
The analysis of defects and defect dynamics in crystalline materials is important for fundamental science and for a wide range of applied engineering. With increasing system size the analysis of molecular-dynamics simulation data becomes non-trivial. Here, we present a workflow for semi-automatic identification and classification of defects in crystalline structures, combining a new approach for defect description with several already existing open-source software packages. Our approach addresses the key challenges posed by the often relatively tiny volume fraction of the modified parts of the sample, thermal motion and the presence of potentially unforeseen atomic configurations (defect types) after irradiation. The local environment of any atom is converted into a rotation-invariant descriptive vector ('fingerprint'), which can be compared to known defect types and also yields a distance metric suited for classification. Vectors which cannot be associated to known structures indicate new types of defects. As proof-of-concept we apply our method on an iron sample to analyze the defects caused by a collision cascade induced by a 10 keV primary-knock-on-atom. The obtained results are in good agreement with reported literature values.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
