论文标题
自动选择和分类原子环境和环境描述符压缩的最大音量单纯量方法
Maximum volume simplex method for automatic selection and classification of atomic environments and environment descriptor compression
论文作者
论文摘要
指纹距离测量原子环境的相似性，通常是根据原子环境指纹向量计算得出的。在这项工作中，我们介绍了可以执行反操作的单纯形方法，即从指纹距离计算指纹向量。以这种方式发现的指纹向量指向单纯形的角落。对于大量数据集，我们可以找到一个特定最大的音量单纯形，其尺寸给出了指纹矢量空间的有效尺寸。我们表明，这种单纯形的角落对应于地标环境，这些环境可以通过全自动的方式来分析结构。通过这种方式，我们可以在晶界或碳片边缘检测原子，而没有任何人类对预期环境的投入。通过将指纹投影到最大的单纯量上，我们还可以获得比原始的指纹矢量要短得多，但其信息含量并未大大减少。
Fingerprint distances, which measure the similarity of atomic environments, are commonly calculated from atomic environment fingerprint vectors. In this work we present the simplex method which can perform the inverse operation, i.e. calculating fingerprint vectors from fingerprint distances. The fingerprint vectors found in this way point to the corners of a simplex. For a large data set of fingerprints, we can find a particular largest volume simplex, whose dimension gives the effective dimension of the fingerprint vector space. We show that the corners of this simplex correspond to landmark environments that can by used in a fully automatic way to analyse structures. In this way we can for instance detect atoms in grain boundaries or on edges of carbon flakes without any human input about the expected environment. By projecting fingerprints on the largest volume simplex we can also obtain fingerprint vectors that are considerably shorter than the original ones but whose information content is not significantly reduced.
