%0 journal article
%@ 2397-2106
%A Özkan, C., Sahlmann, L., Feiler, C., Zheludkevich, M., Lamaka, S., Sewlikar, P., Kooijman, A., Taheri, P., Mol, A.

%D 2024
%J npj Materials Degradation

%P 21 
%R doi:10.1038/s41529-024-00435-z
%T Laying the experimental foundation for corrosion inhibitor discovery through machine learning
%U https://doi.org/10.1038/s41529-024-00435-z
 
%X Creating durable, eco-friendly coatings for long-term corrosion protection requires innovative strategies to streamline design and development processes, conserve resources, and decrease maintenance costs. In this pursuit, machine learning emerges as a promising catalyst, despite the challenges presented by the scarcity of high-quality datasets in the field of corrosion inhibition research. To address this obstacle, we have created an extensive electrochemical library of around 80 inhibitor candidates. The electrochemical behaviour of inhibitor-exposed AA2024-T3 substrates was captured using linear polarisation resistance, electrochemical impedance spectroscopy, and potentiodynamic polarisation techniques at different exposure times to obtain the most comprehensive electrochemical picture of the corrosion inhibition over a 24-h period. The experimental results yield target parameters and additional input features that can be combined with computational descriptors to develop quantitative structure–property relationship (QSPR) models augmented by mechanistic input features.