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- Title
Laying the experimental foundation for corrosion inhibitor discovery through machine learning.
- Authors
Özkan, Can; Sahlmann, Lisa; Feiler, Christian; Zheludkevich, Mikhail; Lamaka, Sviatlana; Sewlikar, Parth; Kooijman, Agnieszka; Taheri, Peyman; Mol, Arjan
- 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.
- Subjects
CORROSION &; anti-corrosives; ELECTROLYTIC corrosion; IMPEDANCE spectroscopy; MAINTENANCE costs; MACHINE learning; DESCRIPTOR systems
- Publication
NPJ Materials Degradation, 2024, Vol 8, Issue 1, p1
- ISSN
2397-2106
- Publication type
Article
- DOI
10.1038/s41529-024-00435-z