Show simple item record

dc.contributor.authorChen, Fiona Fang
dc.contributor.authorBreedon, Michael
dc.contributor.authorWhite, Paul
dc.contributor.authorChu, Clement
dc.contributor.authorMallick, Dwaipayan
dc.contributor.authorThomas, Sebastian
dc.contributor.authorSapper, Erik
dc.contributor.authorCole, Ivan
dc.date.accessioned2018-07-26T04:17:08Z
dc.date.available2018-07-26T04:17:08Z
dc.date.issued2016
dc.identifier.issn0264-1275
dc.identifier.doi10.1016/j.matdes.2016.09.084
dc.identifier.urihttp://hdl.handle.net/10072/173716
dc.description.abstractThe increasing demand for environmentally-friendly and non-toxic coating systems from the aerospace and heavy industry sectors is driving innovation in corrosion inhibitor design and functional coating development. A fundamental understanding of how molecular structure and functionality influences the electrochemical responses of inhibited coatings is crucial for the design of effective functional coatings to replace stalwart, yet highly toxic industrial solutions. In this paper, an artificial neural network approach is presented to quantitatively study the relationship between the structural/molecular features of inhibitor compounds and their experimentally measured electrochemical properties. The presented method is applied to correlate molecular features of corrosion inhibitors with experimentally obtained corrosion potential (Ecorr), corrosion current (Icorr) and anodic/cathodic Tafel slopes. The neural network model, trained through an automatic optimization process, was able to predict the electrochemical performance for a given inhibitor molecule candidate. We will demonstrate how it can be utilised to assess the impact of molecular structure on the final effectiveness of the candidate corrosion inhibitor molecule. The presented neural network learning method could be applied to other areas in materials science for accelerating general materials discovery and functional coating design.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofpagefrom410
dc.relation.ispartofpageto418
dc.relation.ispartofjournalMaterials and Design
dc.relation.ispartofvolume112
dc.subject.fieldofresearchMaterials Engineering not elsewhere classified
dc.subject.fieldofresearchManufacturing Engineering
dc.subject.fieldofresearchMaterials Engineering
dc.subject.fieldofresearchMechanical Engineering
dc.subject.fieldofresearchcode091299
dc.subject.fieldofresearchcode0910
dc.subject.fieldofresearchcode0912
dc.subject.fieldofresearchcode0913
dc.titleCorrelation between molecular features and electrochemical properties using an artificial neural network
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.hasfulltextNo Full Text
gro.griffith.authorCole, Ivan


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record