Data-driven corrosion inhibition efficiency prediction model incorporating 2D–3D molecular graphs and inhibitor concentration

Jinbo Ma, Jiaxin Dai, Xin Guo, Dongmei Fu*, Lingwei Ma, Patrick Keil, Arjan Mol, Dawei Zhang

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

Following the construction of a dataset of cross-category corrosion inhibitors at different concentrations based on 1241 data from 184 research papers, a performance prediction model incorporating 2D–3D molecular graph representation and corrosion inhibitor concentration information was established. This model was shown to effectively predict the inhibition efficiency (IE) of different categories of corrosion inhibitors for carbon steel in 1 mol/L HCl solution. The model was also able to predict IEs of corrosion inhibitors at different concentrations. The results demonstrated that 3D features of corrosion inhibitors, especially those of large molecules, had a significant impact on the prediction precision of IEs.

Original languageEnglish
Article number111420
Number of pages7
JournalCorrosion Science
Volume222
DOIs
Publication statusPublished - 2023

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Corrosion inhibition
  • Corrosion prediction
  • Machine learning
  • Molecular graph

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