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  • Correlation between molecular features and electrochemical properties using an artificial neural network

    Author(s)
    Chen, Fiona Fang
    Breedon, Michael
    White, Paul
    Chu, Clement
    Mallick, Dwaipayan
    Thomas, Sebastian
    Sapper, Erik
    Cole, Ivan
    Griffith University Author(s)
    Cole, Ivan
    Year published
    2016
    Metadata
    Show full item record
    Abstract
    The 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 ...
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    The 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.
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    Journal Title
    Materials and Design
    Volume
    112
    DOI
    https://doi.org/10.1016/j.matdes.2016.09.084
    Subject
    Manufacturing engineering
    Materials engineering
    Materials engineering not elsewhere classified
    Mechanical engineering
    Publication URI
    http://hdl.handle.net/10072/173716
    Collection
    • Journal articles

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