Nanoscale Phase Identification Using Two-Dimensional Pair Correlation Functions: A Case Study on Hafnium Oxide

Accurate identification of local phases in nanocrystalline materials is essential for understanding their functional properties, but it remains a significant challenge for polymorphic materials to locally differentiate them at nanoscale. This challenge is further compounded in polycrystalline materials with randomly oriented grains and the coexistence of multiple phases. In this report, we present a methodology for phase and orientation identification at the nanoscale by leveraging vector pair correlation functions extracted from atomically resolved scanning transmission electron microscopy (STEM) images. We demonstrate the accuracy of the methodology on both simulated and experimental data from HfO2-based films, a material that exhibits multiple coexisting phases in films with thicknesses ranging from 5 to 20 nm. While demonstrated on HfO2 films, the methodology can be extended to other polymorphic nanocrystalline systems with complex phase coexistence.

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Publication Details

Journal
Microscopy and Microanalysis
Published
2025-09-03
DOI
https://doi.org/10.1093/mam/ozaf083
Citations
1
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.52

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article

Nanoscale Phase Identification Using Two-Dimensional Pair Correlation Functions: A Case Study on Hafnium Oxide

Elizabeth C. Dickey, Charles Evans, Jon F. Ihlefeld, Samantha T. Jaszewski et al.
1 citations
Microscopy and Microanalysis
Machine Learning in Materials Science
0.52
article

Nanoscale Phase Identification Using Two-Dimensional Pair Correlation Functions: A Case Study on Hafnium Oxide

Elizabeth C. Dickey, Charles Evans, Jon F. Ihlefeld, Samantha T. Jaszewski, Sebastián Calderón
article en
1 citations

Abstract

Accurate identification of local phases in nanocrystalline materials is essential for understanding their functional properties, but it remains a significant challenge for polymorphic materials to locally differentiate them at nanoscale. This challenge is further compounded in polycrystalline materials with randomly oriented grains and the coexistence of multiple phases. In this report, we present a methodology for phase and orientation identification at the nanoscale by leveraging vector pair correlation functions extracted from atomically resolved scanning transmission electron microscopy (STEM) images. We demonstrate the accuracy of the methodology on both simulated and experimental data from HfO2-based films, a material that exhibits multiple coexisting phases in films with thicknesses ranging from 5 to 20 nm. While demonstrated on HfO2 films, the methodology can be extended to other polymorphic nanocrystalline systems with complex phase coexistence.

Microscopy and MicroanalysisVol. 31(5)
McCormick (United States) (US), University of Virginia (US), Carnegie Mellon University (US)
U.S. Department of Energy, Carnegie Mellon University, Office of Science, Energy Frontier Research Centers
Openalex Percentile: Top 33%
Machine Learning in Materials Science
0.52
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