materialscloud:2021.218
Published December 13, 2021 | Version v1
Dataset Open

Maximum volume simplex method for automatic selection and classification of atomic environments and environment descriptor compression

Creators

  • 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

* Contact person

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.

Files

File preview

All files

Files (11.9 MiB)

Name Size
md5:6864c4b9d077621f804cae5a4dd4adac
448 Bytes Preview Download
md5:7f262660740ea4885bc86ea7c045fc56
11.1 MiB Preview Download
md5:f01f19cc048ebfc08b5cc751f4671b4a
840.0 KiB Preview Download

References

Journal reference
Parsaeifard, Behnam, et al. "Maximum volume simplex method for automatic selection and classification of atomic environments and environment descriptor compression." The Journal of Chemical Physics 153.21 (2020): 214104., doi: 10.1063/5.0030061

Journal reference
Parsaeifard, Behnam, et al. "Maximum volume simplex method for automatic selection and classification of atomic environments and environment descriptor compression." The Journal of Chemical Physics 153.21 (2020): 214104.

Journal reference (Paper where the data is discussed.)
Parsaeifard, Behnam, et al. "Fingerprint-Based Detection of Non-Local Effects in the Electronic Structure of a Simple Single Component Covalent System." Condensed Matter 6.1 (2021): 9., doi: 10.3390/condmat6010009

Journal reference (Paper where the data is discussed.)
Parsaeifard, Behnam, et al. "Fingerprint-Based Detection of Non-Local Effects in the Electronic Structure of a Simple Single Component Covalent System." Condensed Matter 6.1 (2021): 9.

Journal reference (Paper where the data is discussed.)
Parsaeifard, Behnam, et al. "An assessment of the structural resolution of various fingerprints commonly used in machine learning." Machine Learning: Science and Technology 2.1 (2021): 015018., doi: 10.1088/2632-2153/abb212

Journal reference (Paper where the data is discussed.)
Parsaeifard, Behnam, et al. "An assessment of the structural resolution of various fingerprints commonly used in machine learning." Machine Learning: Science and Technology 2.1 (2021): 015018.