论文标题
ClassOmfier：用于晶格缺陷聚类分析和检测的神经网络
ClasSOMfier: A neural network for cluster analysis and detection of lattice defects
论文作者
论文摘要
ClassOmfier是一个软件包，将原子分类为给定数量的断开组（或簇），并检测晶格缺陷，例如空位，间隙，错位，空隙，空隙和晶界。每个簇都是由原子环境可以通过共同模式描述的原子形成的。与文献中可用的许多方法不同，这些模式是事先给出的，并且与已知的晶格结构（即FCC，BCC或HCP）相关联，该代码实现了Kohonen Network，该网络基于无处不在的学习，并且在没有有关原子环境的信息中必须提前提供有关原子环境的信息。 ClassOmfier通过在Python中使用用户友好的界面提供了有效而快速的代码来加速机器学习在集群分析中的应用。
ClasSOMfier is a software package to classify atoms into a given number of disconnected groups (or clusters) and detect lattice defects, such as vacancies, interstitials, dislocations, voids and grain boundaries. Each cluster is formed by atoms whose atomic environment can be described by a common pattern. Unlike many methods available in the literature, where these patterns are given in advance and are associated with known lattice structures (i.e. fcc, bcc or hcp), this code implements a Kohonen network, which is based on unsupervised learning and where no information about the atomic environment has to be given in advance. ClasSOMfier accelerates the application of machine learning for cluster analysis by providing an efficient and fast code in Fortran with a user-friendly interface in Python.
