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

Fiona Fang Chen, Michael Breedon, Paul White, Clement Chu, Dwaipayan Mallick, Sebastian Thomas, Erik Sapper, Ivan Cole

Research output: Contribution to journalArticleResearchpeer-review

29 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)410-418
Number of pages9
JournalMaterials & Design
Publication statusPublished - 15 Dec 2016


  • Artificial neural network
  • Corrosion inhibitor
  • Electrochemical property
  • Molecular modelling
  • Molecular structure

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