Cross-category prediction of corrosion inhibitor performance based on molecular graph structures via a three-level message passing neural network model

Jiaxin Dai, Dongmei Fu*, Guangxuan Song, Lingwei Ma, Xin Guo, Arjan Mol, Ivan Cole, Dawei Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
12 Downloads (Pure)

Abstract

Current experimental verification, computational modeling, and machine learning methods for predicting corrosion inhibition efficiency (IE) are limited to specific inhibitor categories with high cost and poor generalization. In this study, a cross-category corrosion inhibitor dataset is constructed and a three-level direct message passing neural network (3 L–DMPNN) model using molecular structure information that integrates atomic-level, chemical bond-level, and molecular-level features to predict the IEs of compounds in a specific environment is established. This work demonstrates that the 3 L–DMPNN model can predict IEs of cross-category corrosion inhibitors from other independent literature and experimental dataset effectively and quickly.

Original languageEnglish
Article number110780
Number of pages12
JournalCorrosion Science
Volume209
DOIs
Publication statusPublished - 2022

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 inhibitors
  • Machine learning
  • Message passing neural network
  • Molecular structure
  • SMILES

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