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Unveiling and Mapping Polymorphs in Fluorite Y2TiO5 Using 4D-STEM and Unsupervised Machine Learning: Article No. e20309

  • Eitan Hershkovitz
  • , Timothy Yoo
  • , Xiaofei Pu
  • , Kaustubh Bawane
  • , Tadachika Nakayama
  • , Hisayuki Suematsu
  • , Lingfeng He
  • , Honggyu Kim
  • University of Florida
  • Idaho National Laboratory
  • Nagaoka University of Technology
  • North Carolina State University

Research output: Contribution to journalArticlepeer-review

1 Scopus Citations

Abstract

Y2TiO5 belongs to the Ln2TiO5 (Ln = lanthanide or Y) family of ceramic materials and exhibits a range of desirable material properties such as radiation tolerance, frustrated magnetism, and large dielectric constant. However, understanding the complex crystal structure of Y2TiO5 remains elusive, given that Y2TiO5 can adopt multiple polymorphs such as cubic, orthorhombic, and hexagonal phases within the lattice. In this work, we report a detailed structural analysis of Y2TiO5 using four-dimensional scanning transmission electron microscopy coupled with unsupervised machine learning. The pyrochlore nanodomains, characterized by the ordered arrangement of yttrium cations on the A site of their A2BO5 structure, are present within the matrix of a predominantly fluorite-structured Y2TiO5 along with a third polymorph, the hexagonal phase. The pyrochlore phase is found to form 2 nm boundary regions around hexagonal phase stacking faults, highlighting the potential influence of the hexagonal phase on the occurrence and distribution of the pyrochlore phase. Lastly, we identify a unique pyrochlore phase with asymmetric arrangement of cation ordering along a single planar direction. Our findings provide invaluable insights into the possible mechanisms stabilizing pyrochlore nanodomains within the fluorite lattice of Y2TiO5.
Original languageAmerican English
Number of pages13
JournalJournal of the American Ceramic Society
Volume108
Issue number4
DOIs
StatePublished - 2025

NLR Publication Number

  • NREL/JA-5700-96763

Keywords

  • 4D-STEM
  • fluorite
  • pyrochlore
  • unsupervised machine learning
  • yttrium titanates

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