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A Graph-Based k-Nearest Neighbor (KNN) Approach for Predicting Phases in High-Entropy Alloys

Ghouchan Nezhad Noor Nia, Raheleh; Jalali, Mehrdad ORCID iD icon 1; Houshmand, Mahboobeh
1 Institut für Funktionelle Grenzflächen (IFG), Karlsruher Institut für Technologie (KIT)

Abstract:

Traditional techniques for detecting materials have been unable to coordinate with the advancement of material science today due to their low accuracy and high cost. Accordingly, machine learning (ML) improves prediction efficiency in material science and high-entropy alloys’ (HEAs’) phase prediction. Unlike traditional alloys, HEAs consist of at least five elements with equal or near-equal atomic sizes. In a previous approach, we presented an HEA interaction network based on its descriptors. In this study, the HEA phase is predicted using a graph-based k-nearest neighbor (KNN) approach. Each HEA compound has its phase, which includes five categories: FCC, BCC, HCP, Multiphase and Amorphous. A composition phase represents a state of matter with a certain energy level. Phase prediction is effective in determining its application. Each compound in the network has some neighbors, and the phase of a new compound can be predicted based on the phase of the most similar neighbors. The proposed approach is performed on the HEA network. The experimental results show that the accuracy of the proposed approach for predicting the phase of new alloys is 88.88%, which is higher than that of other ML methods.


Verlagsausgabe §
DOI: 10.5445/IR/1000150601
Veröffentlicht am 12.09.2022
Originalveröffentlichung
DOI: 10.3390/app12168021
Scopus
Zitationen: 7
Web of Science
Zitationen: 7
Dimensions
Zitationen: 10
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Funktionelle Grenzflächen (IFG)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 10.08.2022
Sprache Englisch
Identifikator ISSN: 2076-3417
KITopen-ID: 1000150601
HGF-Programm 43.33.11 (POF IV, LK 01) Adaptive and Bioinstructive Materials Systems
Erschienen in Applied Sciences
Verlag MDPI
Band 12
Heft 16
Seiten Art.-Nr.: 8021
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Nachgewiesen in Web of Science
Dimensions
Scopus
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