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Revealing in-plane grain boundary composition features through machine learning from atom probe tomography data

  • Grain boundaries (GBs) are planar lattice defects that govern the properties of many types of polycrystalline materials. Hence, their structures have been investigated in great detail. However, much less is known about their chemical features, owing to the experimental difficulties to probe these features at the atomic length scale inside bulk material specimens. Atom probe tomography (APT) is a tool capable of accomplishing this task, with an ability to quantify chemical characteristics at near-atomic scale. Using APT data sets, we present here a machine-learning-based approach for the automated quantification of chemical features of GBs. We trained a convolutional neural network (CNN) using twenty thousand synthesized images of grain interiors, GBs, or triple junctions. Such a trained CNN automatically detects the locations of GBs from APT data. Those GBs are then subjected to compositional mapping and analysis, including revealing their in-plane chemical decoration patterns. WeGrain boundaries (GBs) are planar lattice defects that govern the properties of many types of polycrystalline materials. Hence, their structures have been investigated in great detail. However, much less is known about their chemical features, owing to the experimental difficulties to probe these features at the atomic length scale inside bulk material specimens. Atom probe tomography (APT) is a tool capable of accomplishing this task, with an ability to quantify chemical characteristics at near-atomic scale. Using APT data sets, we present here a machine-learning-based approach for the automated quantification of chemical features of GBs. We trained a convolutional neural network (CNN) using twenty thousand synthesized images of grain interiors, GBs, or triple junctions. Such a trained CNN automatically detects the locations of GBs from APT data. Those GBs are then subjected to compositional mapping and analysis, including revealing their in-plane chemical decoration patterns. We applied this approach to experimentally obtained APT data sets pertaining to three case studies, namely, Ni-P, Pt-Au, and Al-Zn-Mg-Cu alloys. In the first case, we extracted GB specific segregation features as a function of misorientation and coincidence site lattice character. Secondly, we revealed interfacial excesses and in-plane chemical features that could not have been found by standard compositional analyses. Lastly, we tracked the temporal evolution of chemical decoration from early-stage solute GB segregation in the dilute limit to interfacial phase separation, characterized by the evolution of complex composition patterns. This machine-learning-based approach provides quantitative, unbiased, and automated access to GB chemical analyses, serving as an enabling tool for new discoveries related to interface thermodynamics, kinetics, and the associated chemistry-structure-property relations.zeige mehrzeige weniger

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Metadaten
Autor*innen:X. Zhou, Y. Wei, M. Kühbach, H. Zhao, F. Vogel, Reza Darvishi KamachaliORCiD, G. B. Thompson, D. Raabe, B. Gault
Dokumenttyp:Zeitschriftenartikel
Veröffentlichungsform:Verlagsliteratur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):Acta materialia
Jahr der Erstveröffentlichung:2022
Organisationseinheit der BAM:5 Werkstofftechnik
5 Werkstofftechnik / 5.2 Metallische Hochtemperaturwerkstoffe
5 Werkstofftechnik / 5.5 Materialmodellierung
Veröffentlichende Institution:Bundesanstalt für Materialforschung und -prüfung (BAM)
Verlag:Elsevier
Verlagsort:Amsterdam
Jahrgang/Band:226
Aufsatznummer:117633
Erste Seite:1
Letzte Seite:15
DDC-Klassifikation:Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurwissenschaften und zugeordnete Tätigkeiten
Freie Schlagwörter:Alloy microstructure; Digitalization; Machine learning
Themenfelder/Aktivitätsfelder der BAM:Material
Material / Degradation von Werkstoffen
Material / Materialdesign
DOI:10.1016/j.actamat.2022.117633
URN:urn:nbn:de:kobv:b43-543049
ISSN:1359-6454
Verfügbarkeit des Dokuments:Datei für die Öffentlichkeit verfügbar ("Open Access")
Lizenz (Deutsch):License LogoCreative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International
Datum der Freischaltung:11.02.2022
Referierte Publikation:Ja
Datum der Eintragung als referierte Publikation:09.06.2022
Schriftenreihen ohne Nummerierung:Wissenschaftliche Artikel der BAM
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