2026-04-16T23:46:38Zhttps://www.radar-service.eu/oai/OAIHandler10.35097/18022025-04-17T12:18:48Zradar4kit
10.35097/1802
Kang, Sangjun
Sangjun
Kang
0000-0002-5096-5965
Institut für Nanotechnologie (INT), Karlsruher Institut für Technologie (KIT)
Wollersen, Vanessa
Vanessa
Wollersen
Institut für Nanotechnologie (INT), Karlsruher Institut für Technologie (KIT)
Minnert, Christian
Christian
Minnert
Technische Universität Darmstadt (TU Darmstadt)
Durst, Karsten
Karsten
Durst
Technische Universität Darmstadt (TU Darmstadt)
Kim, Hyoung Seop
Hyoung Seop
Kim
Kuebel, Christian
Christian
Kuebel
0000-0001-5701-4006
Institut für Nanotechnologie (INT), Karlsruher Institut für Technologie (KIT)
Mu, Xiaoke
Xiaoke
Mu
Institut für Nanotechnologie (INT), Karlsruher Institut für Technologie (KIT)
Mapping local atomic structure of metallic glasses using machine learning aided 4D-STEM
Karlsruhe Institute of Technology
2022
2023
Engineering
Four dimensional-scanning transmission electron microscopy (4D-STEM)
Pair distribution function (PDF)
Nonnegative matrix factorization (NMF)
Metallic glasses
Open Access
Creative Commons Attribution 4.0 International
Kang, Sangjun
0000-0002-5096-5965
Wollersen, Vanessa
Minnert, Christian
Durst, Karsten
Kim, Hyoung Seop
Kuebel, Christian
0000-0001-5701-4006
Mu, Xiaoke
Amorphous materials, e.g., polymers, metallic and oxidic glasses, consist of heterogeneous atomic/molecular packing at the nanoscale. Spatial variation of the local structure plays an important role in determining material properties. Experimentally probing the local atomic structure within the amorphous phase has been one of the main challenges for material research. Here, we present a new approach to characterize the local atomic structure and map structural variants in the amorphous phase using machine learning (ML) aided four dimensional-scanning transmission electron microscopy (4D-STEM). We utilized nonnegative matrix factorization (NMF) to identify the local structural types of metallic glasses from the 4D-STEM dataset. Using Fe-based metallic glasses as a model system, we demonstrate that two basic structural types, one with a more liquid-like and another with a more solid-like structure, are distributed throughout the glass with a characteristic length scale of a few nanometers. Thermal annealing induces a change in their distribution and relative population but without the appearance of any additional phase. This provides new insight into the relaxation phenomena of metallic glass and solid experimental evidence for the theoretical hypothesis on atomic packing in glassy structures.
Raw data for the publication "Mapping local atomic structure of metallic glasses using machine learning aided 4D-STEM"
https://publikationen.bibliothek.kit.edu/1000164027
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