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

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dc.contributor.authorQuadri, Taiwo W.-
dc.contributor.authorAkpan, Ekemini D.-
dc.contributor.authorElugoke, Saheed E.-
dc.contributor.authorOlasunkanmi, Lukman O.-
dc.contributor.authorSheetal, Ashish Kumar-
dc.contributor.authorSingh, Ashish Kumar-
dc.contributor.authorPani, Balaram-
dc.contributor.authorTuteja, Jaya-
dc.contributor.authorShukla, Sudhish Kumar-
dc.contributor.authorVerma, Chandrabhan-
dc.contributor.authorLgaz, Hassane-
dc.contributor.authorAnadebe, Valentine Chikaodili-
dc.contributor.authorBarik, Rakesh Chandra-
dc.contributor.authorGuo, Lei-
dc.contributor.authorAlfantazi, Akram-
dc.contributor.authorMothudi, Bakang M.-
dc.contributor.authorEbenso, Eno E.-
dc.date.accessioned2025-04-24T02:01:18Z-
dc.date.available2025-04-24T02:01:18Z-
dc.date.issued2025-03-
dc.identifier.issn1931-9401-
dc.identifier.issn1931-9401-
dc.identifier.urihttps://scholarworks.bwise.kr/erica/handle/2021.sw.erica/125112-
dc.description.abstractArtificial 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.-
dc.language영어-
dc.language.isoENG-
dc.publisherAIP Publishing-
dc.titleState-of-the-art progress on artificial intelligence and machine learning in accessing molecular coordination and adsorption of corrosion inhibitors-
dc.typeArticle-
dc.publisher.location미국-
dc.identifier.doi10.1063/5.0228503-
dc.identifier.scopusid2-s2.0-85214353958-
dc.identifier.wosid001390808000001-
dc.identifier.bibliographicCitationAPPLIED PHYSICS REVIEWS, v.12, no.1-
dc.citation.titleAPPLIED PHYSICS REVIEWS-
dc.citation.volume12-
dc.citation.number1-
dc.type.docTypeReview-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaPhysics-
dc.relation.journalWebOfScienceCategoryPhysics, Applied-
dc.subject.keywordPlusGENETIC FUNCTION APPROXIMATION-
dc.subject.keywordPlusATMOSPHERIC CORROSION-
dc.subject.keywordPlusFEATURE-SELECTION-
dc.subject.keywordPlusNEURAL-NETWORKS-
dc.subject.keywordPlusQUANTITATIVE STRUCTURE-
dc.subject.keywordPlusPITTING CORROSION-
dc.subject.keywordPlusMILD-STEEL-
dc.subject.keywordPlusADVANCED STATISTICS-
dc.subject.keywordPlusLINEAR-REGRESSION-
dc.subject.keywordPlusPREDICTIVE MODELS-
dc.identifier.urlhttps://pubs.aip.org/aip/apr/article-abstract/12/1/011302/3329291/State-of-the-art-progress-on-artificial?redirectedFrom=fulltext-
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ERICA부총장 한양인재개발원 (ERICA 창의융합교육원)
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