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Cited by 60Machine Learning in Materials ScienceCrystal Structures and PropertiesCrystal structures of chemical compounds

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AFLOW-XtalFinder: a reliable choice to identify crystalline prototypes

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Hicks, David, Toher, Cormac, Ford, Denise C., Rose, Frisco, Santo, Carlo De, Levy, Ohad, Mehl, Michael J., Curtarolo, Stefano·2021·npj Computational Materials
DOI 10.1038/s41524-020-00483-4
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Abstract

The accelerated growth rate of repository entries in crystallographic databases makes it arduous to identify and classify their prototype structures. The open-source AFLOW-XtalFinder package was developed to solve this problem. It symbolically maps structures into standard designations following the AFLOW Prototype Encyclopedia and calculates the internal degrees of freedom consistent with the International Tables for Crystallography. To ensure uniqueness, structures are analyzed and compared via symmetry, local atomic geometries, and crystal mapping techniques, simultaneously grouping them by similarity. The software (i) distinguishes distinct crystal prototypes and atom decorations, (ii) determines equivalent spin configurations, (iii) reveals compounds with similar properties, and (iv) guides the discovery of unexplored materials. The operations are accessible through a Python module ready for workflows, and through command line syntax. All the 4+ million compounds in the AFLOW.org repositories are mapped to their ideal prototype, allowing users to search database entries via symbolic structure-type. Furthermore, 15,000 unique structures — sorted by prevalence — are extracted from the AFLOW-ICSD catalog to serve as future prototypes in the Encyclopedia.

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  • Discovered stable crystals correspond to more than 45,500 novel prototypes as measured by XtalFinder, a 5.6 times increase from 8,000 of the Materials Project.

    “Clustering by means of prototype analysis39 supports the diversity of discovered crystals with GNoME, leading to more than 45,500 novel prototypes in Fig. 2c (a 5.6 times increase from 8,000 of the Materials Project), which could not have arisen from full substitutions or prototype enumeration.”

    From Scaling deep learning for materials discovery (2023) · cites this paper as [39]

  • Novel discoveries are validated using XtalFinder, using the compare_structures function available from the command line.

    “We validate the novel discoveries using XtalFinder (ref. 39), using the compare_structures function available from the command line.”

    From Scaling deep learning for materials discovery (2023) · cites this paper as [39]

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Metadata

Authors
Hicks, D., Toher, C., Ford, D. C., Rose, F., Santo, C. D., Levy, O., Mehl, M. J., & Curtarolo, S.
Affiliations
Duke University (US)
ORCID
Venue
npj Computational Materials
Publisher
Springer Science and Business Media LLC
Published
2021-02-11
Volume
7
Issue
1
ISSN
2057-3960 (ISSN-L)
DOI
10.1038/s41524-020-00483-4
OpenAlex
W3091857758
Cited by
60
References
4
Citations by year
2026: 15 · 2025: 8 · 2024: 11 · 2023: 11 · 2022: 12 · 2021: 3
FWCI
2.63
Type
journal article
Language
en
Open access
Licence
Locations
  • npj Computational Materials (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University)
Funders
  • U.S. Department of Defense (N000141712090)
  • Alexander von Humboldt-Stiftung
  • National Defense Science and Engineering Graduate
  • Office of Naval Research (N00014-17-1, N00014-17-1-2090, N00014, N00014-17)
Topics
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Physical Sciences › Materials Science › Electronic, Optical and Magnetic Materials › Crystal Structures and Properties
  • Physical Sciences › Chemistry › Inorganic Chemistry › Crystal structures of chemical compounds
Keywords
crystal structure classification, International Tables for Crystallography, crystal symmetry, spin configurations, crystallographic databases, materials discovery, open source software

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