Data-driven corrosion inhibition efficiency prediction model incorporating 2D–3D molecular graphs and inhibitor concentration
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)
Arjan Mol (TU Delft - Team Arjan Mol)
Dawei Zhang (University of Science and Technology Beijing, Liaoning Academy of Materials)
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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.