@misc{zkan_laying_the_2024, 
author={Özkan, C., Sahlmann, L., Feiler, C., Zheludkevich, M., Lamaka, S., Sewlikar, P., Kooijman, A., Taheri, P., Mol, A.},
title={Laying the experimental foundation for corrosion inhibitor discovery through machine learning},
year={2024},
howpublished = {journal article},
doi = {https://doi.org/10.1038/s41529-024-00435-z},
abstract = {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.},
note = {Online available at: \url{https://doi.org/10.1038/s41529-024-00435-z} (DOI). Özkan, C.; Sahlmann, L.; Feiler, C.; Zheludkevich, M.; Lamaka, S.; Sewlikar, P.; Kooijman, A.; Taheri, P.; Mol, A.: Laying the experimental foundation for corrosion inhibitor discovery through machine learning. npj Materials Degradation. 2024. vol. 8, 21. DOI: 10.1038/s41529-024-00435-z}}