Behnam Parsaeifard^1,2*, Daniele Tomerini^1,2*, Deb Sankar De^1,2*, Stefan Goedecker^1,2*

1 Department of Physics, University of Basel, Klingelbergstrasse 82, 4056 Basel, Switzerland

2 National Center for Computational Design and Discovery of Novel Materials (MARVEL), Lausanne, Switzerland

* Corresponding authors emails: behnam.parsaeifard@unibas.ch, daniele.tomerini@gmail.com, debsankar.de@unibas.ch, stefan.goedecker@unibas.ch

DOI10.24435/materialscloud:hj-xe [version v1]

Publication date: Dec 13, 2021

How to cite this record

Behnam Parsaeifard, Daniele Tomerini, Deb Sankar De, Stefan Goedecker, Maximum volume simplex method for automatic selection and classification of atomic environments and environment descriptor compression, Materials Cloud Archive 2021.218 (2021), doi: 10.24435/materialscloud:hj-xe.

Description

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 that 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 dataset of fingerprints, we can find a particular largest 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 be used in a fully automatic way to analyze 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 simplex, we can also obtain fingerprint vectors that are considerably shorter than the original ones but whose information content is not significantly reduced.

Materials Cloud sections using this data

Files

File name	Size	Description
c60-dataset.zip MD5md5:7f262660740ea4885bc86ea7c045fc56	11.1 MiB	The C60 data set used in the paper for the classification and selection of the atomic environments
Fingerprint-main.zip MD5md5:f01f19cc048ebfc08b5cc751f4671b4a	840.0 KiB	The code for the overlap matrix environment and the largest volume simplex developed in the paper. It is taken from the github repository https://github.com/KrumaKruma/Fingerprint

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

Dimensionality Reduction Largest Volume Simplex Overlap Matrix Fingerprint Atomic Environment Selection Classification of Atomic Environments Machine Learning Quantum Non-Local Effects Atomic Fingerprints