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

Journal article (2022)

Authors

Jiaxin Dai University of Science and Technology Beijing
Dongmei Fu University of Science and Technology Beijing
Guangxuan Song University of Science and Technology Beijing
Lingwei Ma University of Science and Technology Beijing
Xin Guo University of Science and Technology Beijing
J.M.C. Mol Team Arjan Mol - Mechanical, Maritime and Materials Engineering
Ivan Cole Royal Melbourne Institute of Technology University
Dawei Zhang University of Science and Technology Beijing
Research Group
Team Arjan Mol (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2022 Jiaxin Dai, Dongmei Fu, Guangxuan Song, Lingwei Ma, Xin Guo, J.M.C. Mol, Ivan Cole, Dawei Zhang
DOI: https://doi.org/10.1016/j.corsci.2022.110780
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Published Date

2022

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Materials Science and Engineering

Research Group

Team Arjan Mol

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.

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