TY - JOUR
T1 - Laying the experimental foundation for corrosion inhibitor discovery through machine learning
AU - Özkan, Can
AU - Sahlmann, Lisa
AU - Feiler, Christian
AU - Zheludkevich, Mikhail
AU - Lamaka, Sviatlana
AU - Sewlikar, Parth
AU - Kooijman, Agnieszka
AU - Taheri, Peyman
AU - Mol, Arjan
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85185541155&partnerID=8YFLogxK
U2 - 10.1038/s41529-024-00435-z
DO - 10.1038/s41529-024-00435-z
M3 - Article
AN - SCOPUS:85185541155
SN - 2397-2106
VL - 8
JO - npj Materials Degradation
JF - npj Materials Degradation
IS - 1
M1 - 21
ER -