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State-of-the-art progress on artificial intelligence and machine learning in accessing molecular coordination and adsorption of corrosion inhibitors

Authors
Quadri, Taiwo W.Akpan, Ekemini D.Elugoke, Saheed E.Olasunkanmi, Lukman O.Sheetal, Ashish KumarSingh, Ashish KumarPani, BalaramTuteja, JayaShukla, Sudhish KumarVerma, ChandrabhanLgaz, HassaneAnadebe, Valentine ChikaodiliBarik, Rakesh ChandraGuo, LeiAlfantazi, AkramMothudi, Bakang M.Ebenso, Eno E.
Issue Date
Mar-2025
Publisher
AIP Publishing
Citation
APPLIED PHYSICS REVIEWS, v.12, no.1
Indexed
SCIE
SCOPUS
Journal Title
APPLIED PHYSICS REVIEWS
Volume
12
Number
1
URI
https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/125112
DOI
10.1063/5.0228503
ISSN
1931-9401
1931-9401
Abstract
Artificial intelligence (AI) and machine learning (ML) have attracted the interest of the research community in recent years. ML has found applications in various areas, especially where relevant data that could be used for algorithm training and retraining are available. In this review article, ML has been discussed in relation to its applications in corrosion science, especially corrosion monitoring and control. ML tools and techniques, ML structure and modeling methods, and ML applications in corrosion monitoring were thoroughly discussed. Furthermore, detailed applications of ML in corrosion inhibitor design/modeling coupled with associated limitations and future perspectives were reported.
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ERICA부총장 한양인재개발원 (ERICA 창의융합교육원)
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