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

Dai, JiaxinFu, DongmeiSong, GuangxuanMa, LingweiGuo, XinMol, ArjanCole, IvanZhang, Dawei2026-06-112026-06-110010-938XORCID:/0000-0001-6582-1457/work/217149571https://hdl.handle.net/1885/733810494Current 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.This work was supported by the Science and Technology Basic Resources Investigation Project (No. 2019FY101404 ).enPublisher Copyright: © 2022 Elsevier LtdCorrosion inhibitorsMachine learningMessage passing neural networkMolecular structureSMILESCross-category prediction of corrosion inhibitor performance based on molecular graph structures via a three-level message passing neural network model202210.1016/j.corsci.2022.11078085140912762