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

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{
  "metadata": {
    "edited_by": 35, 
    "owner": 35, 
    "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.", 
    "keywords": [
      "Dimensionality Reduction", 
      "Largest Volume Simplex", 
      "Overlap Matrix Fingerprint", 
      "Atomic Environment Selection", 
      "Classification of Atomic Environments", 
      "Machine Learning", 
      "Quantum Non-Local Effects", 
      "Atomic Fingerprints"
    ], 
    "is_last": true, 
    "title": "Maximum volume simplex method for automatic selection and classification of atomic environments and environment descriptor compression", 
    "status": "published", 
    "license_addendum": null, 
    "doi": "10.24435/materialscloud:hj-xe", 
    "conceptrecid": "1165", 
    "_files": [
      {
        "checksum": "md5:7f262660740ea4885bc86ea7c045fc56", 
        "description": "The C60 data set used in the paper for the classification and selection of the atomic environments", 
        "key": "c60-dataset.zip", 
        "size": 11635450
      }, 
      {
        "checksum": "md5:f01f19cc048ebfc08b5cc751f4671b4a", 
        "description": "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", 
        "key": "Fingerprint-main.zip", 
        "size": 860159
      }
    ], 
    "references": [
      {
        "citation": "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": "https://doi.org/10.1063/5.0030061", 
        "url": "https://aip.scitation.org/doi/full/10.1063/5.0030061", 
        "type": "Journal reference"
      }, 
      {
        "citation": "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.", 
        "comment": "Paper where the data is discussed.", 
        "url": "https://www.mdpi.com/2410-3896/6/1/9#cite", 
        "doi": "https://doi.org/10.3390/condmat6010009", 
        "type": "Journal reference"
      }, 
      {
        "citation": "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.", 
        "comment": "Paper where the data is discussed.", 
        "url": "https://iopscience.iop.org/article/10.1088/2632-2153/abb212/meta", 
        "doi": "https://doi.org/10.1088/2632-2153/abb212", 
        "type": "Journal reference"
      }
    ], 
    "contributors": [
      {
        "givennames": "Behnam", 
        "affiliations": [
          "Department of Physics, University of Basel, Klingelbergstrasse 82, 4056 Basel, Switzerland", 
          "National Center for Computational Design and Discovery of Novel Materials (MARVEL), Lausanne, Switzerland"
        ], 
        "familyname": "Parsaeifard", 
        "email": "behnam.parsaeifard@unibas.ch"
      }, 
      {
        "givennames": "Daniele", 
        "affiliations": [
          "Department of Physics, University of Basel, Klingelbergstrasse 82, 4056 Basel, Switzerland", 
          "National Center for Computational Design and Discovery of Novel Materials (MARVEL), Lausanne, Switzerland"
        ], 
        "familyname": "Tomerini", 
        "email": "daniele.tomerini@gmail.com"
      }, 
      {
        "givennames": "Deb Sankar", 
        "affiliations": [
          "Department of Physics, University of Basel, Klingelbergstrasse 82, 4056 Basel, Switzerland", 
          "National Center for Computational Design and Discovery of Novel Materials (MARVEL), Lausanne, Switzerland"
        ], 
        "familyname": "De", 
        "email": "debsankar.de@unibas.ch"
      }, 
      {
        "givennames": "Stefan", 
        "affiliations": [
          "Department of Physics, University of Basel, Klingelbergstrasse 82, 4056 Basel, Switzerland", 
          "National Center for Computational Design and Discovery of Novel Materials (MARVEL), Lausanne, Switzerland"
        ], 
        "familyname": "Goedecker", 
        "email": "stefan.goedecker@unibas.ch"
      }
    ], 
    "_oai": {
      "id": "oai:materialscloud.org:1166"
    }, 
    "publication_date": "Dec 13, 2021, 12:54:22", 
    "mcid": "2021.218", 
    "version": 1, 
    "id": "1166", 
    "license": "Creative Commons Attribution 4.0 International"
  }, 
  "revision": 3, 
  "created": "2021-12-12T15:07:55.683126+00:00", 
  "id": "1166", 
  "updated": "2021-12-27T11:32:30.553805+00:00"
}