Structure-mining: Screening structure models by automated fitting to the atomic pair distribution function over large numbers of models

Long Yang, Pavol Juhás, Maxwell W. Terban, Matthew G. Tucker, Simon J.L. Billinge

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

A new approach is presented to obtain candidate structures from atomic pair distribution function (PDF) data in a highly automated way. It fetches, from web-based structural databases, all the structures meeting the experimenter's search criteria and performs structure refinements on them without human intervention. It supports both X-ray and neutron PDFs. Tests on various material systems show the effectiveness and robustness of the algorithm in finding the correct atomic crystal structure. It works on crystalline and nanocrystalline materials including complex oxide nanoparticles and nanowires, low-symmetry and locally distorted structures, and complicated doped and magnetic materials. This approach could greatly reduce the traditional structure searching work and enable the possibility of high-throughput real-time auto-analysis PDF experiments in the future.

Original languageEnglish
Pages (from-to)395-409
Number of pages15
JournalActa Crystallographica Section A: Foundations and Advances
Volume76
DOIs
StatePublished - May 1 2020

Keywords

  • PDF
  • atomic structure
  • automated fitting
  • pair distribution function
  • structure discovery

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