Quantitative three-dimensional imaging of chemical short-range order via machine learning enhanced atom probe tomography

Yue Li (), Ye Wei, Zhangwei Wang (), Xiaochun Liu, Timoteo Colnaghi, Liuliu Han, Ziyuan Rao, Xuyang Zhou, Liam Huber, Raynol Dsouza, Yilun Gong, Jörg Neugebauer, Andreas Marek, Markus Rampp, Stefan Bauer, Hongxiang Li, Ian Baker, Leigh T. Stephenson and Baptiste Gault ()
Additional contact information
Yue Li: Max-Planck Institut für Eisenforschung GmbH
Ye Wei: Max-Planck Institut für Eisenforschung GmbH
Zhangwei Wang: Central South University
Xiaochun Liu: Changsha University of Science and Technology
Timoteo Colnaghi: Max Planck Computing and Data Facility
Liuliu Han: Max-Planck Institut für Eisenforschung GmbH
Ziyuan Rao: Max-Planck Institut für Eisenforschung GmbH
Xuyang Zhou: Max-Planck Institut für Eisenforschung GmbH
Liam Huber: Max-Planck Institut für Eisenforschung GmbH
Raynol Dsouza: Max-Planck Institut für Eisenforschung GmbH
Yilun Gong: Max-Planck Institut für Eisenforschung GmbH
Jörg Neugebauer: Max-Planck Institut für Eisenforschung GmbH
Andreas Marek: Max Planck Computing and Data Facility
Markus Rampp: Max Planck Computing and Data Facility
Stefan Bauer: Max Planck Institute for Intelligent Systems
Hongxiang Li: University of Science and Technology Beijing
Ian Baker: Dartmouth College
Leigh T. Stephenson: Max-Planck Institut für Eisenforschung GmbH
Baptiste Gault: Max-Planck Institut für Eisenforschung GmbH

Nature Communications, 2023, vol. 14, issue 1, 1-11

Abstract: Abstract Chemical short-range order (CSRO) refers to atoms of specific elements self-organising within a disordered crystalline matrix to form particular atomic neighbourhoods. CSRO is typically characterized indirectly, using volume-averaged or through projection microscopy techniques that fail to capture the three-dimensional atomistic architectures. Here, we present a machine-learning enhanced approach to break the inherent resolution limits of atom probe tomography enabling three-dimensional imaging of multiple CSROs. We showcase our approach by addressing a long-standing question encountered in body-centred-cubic Fe-Al alloys that see anomalous property changes upon heat treatment. We use it to evidence non-statistical B2-CSRO instead of the generally-expected D03-CSRO. We introduce quantitative correlations among annealing temperature, CSRO, and nano-hardness and electrical resistivity. Our approach is further validated on modified D03-CSRO detected in Fe-Ga. The proposed strategy can be generally employed to investigate short/medium/long-range ordering phenomena in different materials and help design future high-performance materials.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.nature.com/articles/s41467-023-43314-y Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-43314-y

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-023-43314-y

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().