A quantitative comparison of the fingerprint of twinned microstructures through surface and three-dimensional techniques
Assessing the fingerprint of a material's microstructure is key for supporting materials design. With the emergence of a wide range of 3D characterization techniques, it is critical to understand the main differences in fingerprints reconstructed from 2D and 3D datasets. To this end, we introduce a graph-based microstructure reconstruction framework that enables structural comparisons of twin domain networks in high purity Ti using 3D and 2D electron backscatter diffraction. Insights into the structure of the twin networks are facilitated by combining statistical analysis of twin crystallography with visual and graphical analysis of the novel graph abstractions of the twins. We demonstrate that compared to 3D reconstructions, conventional 2D views of twinning miss key aspects of the microstructure including the high interconnectivity of domains into networks that span the full reconstruction volume. The reduced cross-grain and in-grain twin connectivity typically observed in 2D has notable implications on our understanding of how twinning mediates the plastic response of microstructures and how twin networks evolve. It is thus clear that 3D characterization is critical for accurately inferring both twin network morphologies as well as the key unit processes facilitating network formation.
Authors
- Rodney J. McCabe (ORCID: https://orcid.org/0000-0002-6684-7410)
- Duncan A. Greeley (ORCID: https://orcid.org/0000-0001-8946-313X)
- H.T. Vo (ORCID: https://orcid.org/0000-0003-2206-3013)
- C.N. Tomé (ORCID: https://orcid.org/0000-0003-3405-0500)
- Laurent Capolungo (ORCID: https://orcid.org/0000-0001-8079-7790)
- Matthew M. Schneider (ORCID: https://orcid.org/0000-0002-7425-7736)
Institutions
- Los Alamos National Laboratory (US)
- Los Alamos National Security (United States) (US)
Publication Details
- Journal
- Nature Communications
- Published
- 2025-11-05
- DOI
- https://doi.org/10.1038/s41467-025-64740-0
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- U.S. Department of Energy
- National Nuclear Security Administration
- Basic Energy Sciences
- Los Alamos National Laboratory