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

Journal article (2023)

Authors

Jinbo Ma University of Science and Technology Beijing
Jiaxin Dai University of Science and Technology Beijing
Xin Guo University of Science and Technology Beijing
Dongmei Fu University of Science and Technology Beijing
Lingwei Ma Liaoning Academy of Materials, University of Science and Technology Beijing
Patrick Keil BASF SE
J.M.C. Mol Team Arjan Mol - Mechanical, Maritime and Materials Engineering
Dawei Zhang University of Science and Technology Beijing, Liaoning Academy of Materials
Research Group
Team Arjan Mol (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2023 Jinbo Ma, Jiaxin Dai, Xin Guo, Dongmei Fu, Lingwei Ma, Patrick Keil, J.M.C. Mol, Dawei Zhang
DOI: https://doi.org/10.1016/j.corsci.2023.111420
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Published Date

2023

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Materials Science and Engineering

Research Group

Team Arjan Mol

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.

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