Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 143722 |
| Fachzeitschrift | Electrochimica acta |
| Jahrgang | 476 |
| Frühes Online-Datum | 27 Dez. 2023 |
| Publikationsstatus | Veröffentlicht - 1 Feb. 2024 |
Abstract
This study demonstrates the use of machine learning as a potential tool to efficiently develop new biomedical alloys with improved corrosion resistance by exploring the whole compositional space in the HfNbTaTiZr system. Owing to the small volume and inherited uncertainty of available corrosion data in the literature, k-fold cross-validation and bootstrapping were used to quantify the uncertainty of models and select a robust one. Potentiodynamic polarization experiments were performed on the predicted composition in simulated body fluid at 37 ± 1 °C for validation, demonstrating the new alloy's superior corrosion properties with a homogeneous microstructure as opposed to the dendritic structure.
ASJC Scopus Sachgebiete
- Chemische Verfahrenstechnik (insg.)
- Chemie (insg.)
- Elektrochemie
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
in: Electrochimica acta, Jahrgang 476, 143722, 01.02.2024.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Machine learning
T2 - informed development of high entropy alloys with enhanced corrosion resistance
AU - Ozdemir, H. C.
AU - Nazarahari, A.
AU - Yilmaz, B.
AU - Canadinc, D.
AU - Bedir, E.
AU - Yilmaz, R.
AU - Unal, U.
AU - Maier, H. J.
N1 - Funding Information: The authors thank Dr. Mustafa Baris Yagci for his assistance with the XPS measurements conducted at the Koc University Surface Science and Technology Center (KUYTAM). D. Canadinc acknowledges the support by Alexander von Humboldt Foundation within the scope of the Humboldt Research Award. H.J. Maier acknowledges financial support by Deutsche Forschungsgemeinschaft (project # 426335750 ).
PY - 2024/2/1
Y1 - 2024/2/1
N2 - This study demonstrates the use of machine learning as a potential tool to efficiently develop new biomedical alloys with improved corrosion resistance by exploring the whole compositional space in the HfNbTaTiZr system. Owing to the small volume and inherited uncertainty of available corrosion data in the literature, k-fold cross-validation and bootstrapping were used to quantify the uncertainty of models and select a robust one. Potentiodynamic polarization experiments were performed on the predicted composition in simulated body fluid at 37 ± 1 °C for validation, demonstrating the new alloy's superior corrosion properties with a homogeneous microstructure as opposed to the dendritic structure.
AB - This study demonstrates the use of machine learning as a potential tool to efficiently develop new biomedical alloys with improved corrosion resistance by exploring the whole compositional space in the HfNbTaTiZr system. Owing to the small volume and inherited uncertainty of available corrosion data in the literature, k-fold cross-validation and bootstrapping were used to quantify the uncertainty of models and select a robust one. Potentiodynamic polarization experiments were performed on the predicted composition in simulated body fluid at 37 ± 1 °C for validation, demonstrating the new alloy's superior corrosion properties with a homogeneous microstructure as opposed to the dendritic structure.
KW - Alloy design
KW - Corrosion
KW - High entropy alloy
KW - Machine learning
KW - Microstructure
UR - http://www.scopus.com/inward/record.url?scp=85181681976&partnerID=8YFLogxK
U2 - 10.1016/j.electacta.2023.143722
DO - 10.1016/j.electacta.2023.143722
M3 - Article
AN - SCOPUS:85181681976
VL - 476
JO - Electrochimica acta
JF - Electrochimica acta
SN - 0013-4686
M1 - 143722
ER -