<?xml version='1.0' encoding='utf-8'?> <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"> <dc:creator>Parsaeifard, Behnam</dc:creator> <dc:creator>Tomerini, Daniele</dc:creator> <dc:creator>De, Deb Sankar</dc:creator> <dc:creator>Goedecker, Stefan</dc:creator> <dc:date>2021-12-13</dc:date> <dc: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.</dc:description> <dc:identifier>https://archive.materialscloud.org/record/2021.218</dc:identifier> <dc:identifier>doi:10.24435/materialscloud:hj-xe</dc:identifier> <dc:identifier>mcid:2021.218</dc:identifier> <dc:identifier>oai:materialscloud.org:1166</dc:identifier> <dc:language>en</dc:language> <dc:publisher>Materials Cloud</dc:publisher> <dc:rights>info:eu-repo/semantics/openAccess</dc:rights> <dc:rights>Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights> <dc:subject>Dimensionality Reduction</dc:subject> <dc:subject>Largest Volume Simplex</dc:subject> <dc:subject>Overlap Matrix Fingerprint</dc:subject> <dc:subject>Atomic Environment Selection</dc:subject> <dc:subject>Classification of Atomic Environments</dc:subject> <dc:subject>Machine Learning</dc:subject> <dc:subject>Quantum Non-Local Effects</dc:subject> <dc:subject>Atomic Fingerprints</dc:subject> <dc:title>Maximum volume simplex method for automatic selection and classification of atomic environments and environment descriptor compression</dc:title> <dc:type>Dataset</dc:type> </oai_dc:dc>