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Machine Learning Approach to Community Detection in a High-Entropy Alloy Interaction Network

Ghouchan Nezhad Noor Nia, Raheleh; Jalali, Mehrdad ORCID iD icon 1; Mail, Matthias 2; Ivanisenko, Yulia 2; Kübel, Christian ORCID iD icon 2
1 Institut für Funktionelle Grenzflächen (IFG), Karlsruher Institut für Technologie (KIT)
2 Institut für Nanotechnologie (INT), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

There is a growing trend toward the use of interaction network methods and algorithms, including community-based detection methods, in various fields of science. The approach is already used in many applications, for example, in social sciences and health informatics to analyze behavioral patterns during the COVID-19 pandemic, protein–protein networks in biological sciences, agricultural science, economy, and so forth. This paper attempts to build interaction networks based on high-entropy alloy (HEA) descriptors in order to discover HEA communities with similar functionality. In addition, these communities could be leveraged to discover new alloys not yet included in the data set without any experimental laboratory effort. This research has been carried out using two community detection algorithms, the Louvain algorithm and the enhanced particle swarm optimization (PSO) algorithm. The data set, which is used in this paper, includes 90 HEAs and 6 descriptors. The results reveal 13 alloy communities, and the accuracy of the results is validated by the modularity. The experimental results show that the method with the PSO-based community detection algorithm can achieve alloy communities with an average accuracy improvement of 0.26 compared to the Louvain algorithm. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000144564
Veröffentlicht am 07.04.2022
DOI: 10.1021/acsomega.2c00317
Zitationen: 2
Web of Science
Zitationen: 2
Zitationen: 2
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Funktionelle Grenzflächen (IFG)
Institut für Nanotechnologie (INT)
Karlsruhe Nano Micro Facility (KNMF)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2022
Sprache Englisch
Identifikator ISSN: 2470-1343
KITopen-ID: 1000144564
HGF-Programm 43.35.01 (POF IV, LK 01) Platform for Correlative, In Situ & Operando Charakterizat.
Weitere HGF-Programme 43.35.04 (POF IV, LK 01) Correlative Data Science
Erschienen in ACS Omega
Verlag American Chemical Society (ACS)
Band 7
Heft 15
Seiten 12978–12992
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Vorab online veröffentlicht am 04.04.2022
Nachgewiesen in Dimensions
Web of Science
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