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.

In the current paper the corrosion inhibition synergy between Ce³⁺ loaded NaY-zeolite (NaY-Ce) and 2-Mercaptobenzothiazole (MBT) loaded Zn-Al layered double hydroxide (LDH-MBT) containers is presented. Solutions and water-borne epoxy coatings containing different ratios of the two loaded carriers were prepared and their protection of AA2024-T3 and damaged coatings characterized using global and local electrochemical techniques and other complementary tools. The coatings containing container-combinations showed exceptional corrosion protection over a broad range of NaY-Ce:LDH-MBT weight ratios, with 90:10 being the optimal. The current work landmarks the need for synergetic inhibiting studies devoted to carrier-inhibitor systems in coatings.