Abstract
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.
| Original language | English |
|---|---|
| Article number | 52 |
| Pages (from-to) | 1-10 |
| Journal | npj Computational Materials |
| Volume | 4 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 1 Dec 2018 |
| MoE publication type | A1 Journal article-refereed |
Funding
We thank Amber Geurts for insightful discussions on database-driven materials science. Computing resources from the Aalto Science-IT project are gratefully acknowledged. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 676580 with The Novel Materials Discovery (NOMAD) Laboratory, a European Centre of Excellence and from the Jenny and Antti Wihuri Foundation. This work was furthermore supported by the Academy of Finland through its Centres of Excellence Programme 2015–2017 under the project number 284621, as well as its Key Project Funding scheme under project number 305632 and by the World Premier International Research Center Initiative (WPI), MEXT, Japan.
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