npj: 分类算法—表面和2D材料的原子结构谱系的自动分类

海归学者发起的公益学术平台

分享信息，整合资源

交流学术，偶尔风月

存储在各种计算材料数据库中的信息量越来越大，利用其中的信息对原子结构作自动化和可验证的结构分类，已是必不可少。

来自芬兰阿尔托大学的一个研究小组开发了一种通用的递归计算方案，用于根据系谱进行结构和原子系统整理分类。该方案以NOMAD存档作为基准，用来在含有异构数据的实际数据库环境中测试分类的准确性。他们具体介绍了用来整理材料结构系谱的结构材料图，叙述了如何实施表面和2D材料的自动分类。他们的分类程序能自动确定结构的维数，再将结构分为表面或2D材料，继而恢复基础单元晶胞，最后识别诸如吸附物等离群原子。这一分类算法不需要明确的搜索模式，即使结构存在缺陷和位错也能工作。对各种原子结构所作的测试结果显示，其能为所有恢复结构特性提供高精度测定。软件功能的执行可以集成在具有原子几何图形的现有数据库上，软件只要采用开源许可证便可免费获得安装启用。

该文近期发表于npj Computational Materials 4: 52 (2018)，英文标题与摘要如下，点击左下角“阅读原文”可以自由获取论文PDF。

Materials structure genealogy and high-throughput topological classification of surfaces and 2D materials

Lauri Himanen, Patrick Rinke & Adam Stuart Foster

Automated and verifiable structural classification for atomistic structures is becoming necessary to cope with the vast amount of information stored in various computational materials databases. Here we present a general recursive scheme for the structural classification of atomistic systems and introduce a structural materials map that can be used to organize the materials structure genealogy. We also introduce our implementation for the automatic classification of two-dimensional structures, especially focusing on surfaces and 2D materials. This classification procedure can automatically determine the dimensionality of a structure, further categorize the structure as a surface or a 2D material, return the underlying unit cell and also identify the outlier atoms, such as adsorbates. The classification scheme does not require explicit search patterns and works even in the presence of defects and dislocations. The classification is tested on a wide variety of atomistic structures and provides a high-accuracy determination for all of the returned structural properties. A software implementation of the classification algorithm is freely available with an open-source license.