Machine learning is a powerful means for the rapid development of high-performance functional materials. In this study, we presented a machine learning workflow for predicting the corrosion resistance of a self-healing epoxy coating containing ZIF-8@Ca microfillers. The orthogonal Latin square method was used to investigate the effects of the molecular weight of the polyetheramine curing agent, molar ratio of polyetheramine to epoxy, molar content of the hydrogen bond unit (UPy-D400), and mass content of the solid microfillers (ZIF-8@Ca microfillers) on the low impedance modulus (lg|Z|_0.01Hz) values of the scratched coatings, generating 32 initial datasets. The machine learning workflow was divided into two stages: In stage I, five models were compared and the random forest (RF) model was selected for the active learning. After 5 cycles of active learning, the RF model achieved good prediction accuracy: coefficient of determination (R ²) = 0.709, mean absolute percentage error (MAPE) = 0.081, root mean square error (RMSE) = 0.685 (lg(Ω·cm²)). In stage II, the best coating formulation was identified by Bayesian optimization. Finally, the electrochemical impedance spectroscopy (EIS) results showed that compared with the intact coating ((4.63 ± 2.08) × 10¹¹ Ω·cm²), the |Z|_0.01Hz value of the repaired coating was as high as (4.40 ± 2.04) × 10¹¹ Ω·cm². Besides, the repaired coating showed minimal corrosion and 3.3% of adhesion loss after 60 days of neutral salt spray testing.

Organic coatings are one of the most used and versatile technologies to mitigate corrosion of metals. However, organic coatings are susceptible to defects and damages that may not be easily detected. If not repaired timely, these defects may develop into major coating failures due to corrosion occurring in the damaged region, thereby limiting the lifetime of the to be protected structure. Thus, the development of smart coatings that can accurately identify corrosion location and reliably recover the damage autonomously is of particular interest. Herein, we reported a robust, corrosion-sensing and self-healing coating which incorporated pH-sensitive ZIF-8-capped CaCO₃ microcontainers containing the healing agent tung oil (TO) and the corrosion indicator/inhibitor 1,10-phenanthrolin-5-amine (APhen). The spontaneous leakage of incorporated TO and APhen was restrained, and the release initiated when local pH variation occurred. The corrosion protection performance of the coatings implanted with different contents of smart microcontainers were evaluated. The intact epoxy coating containing 7.5 wt% of the microcontainers exhibited the best protection performance with low water absorption (0.65 wt%), low O₂ permeability (0.21 × 10^–15 cm³ cm cm⁻² s⁻¹ Pa⁻¹), and a high storage modulus (3.0 GPa). Electrochemical impedance spectroscopy (EIS) measurements in 3.5 wt% NaCl solution demonstrated superior durability of the composite coating after self-healing. The immersion test and neutral salt spray test confirmed the coating can accurately report corrosion sites via coloration.

Herein, we report the development of a self-sensing and active corrosion protection coating which incorporates pH-sensitive multilayer chitosan/alginate-covered CaCO₃ microcontainers containing 1,10-phenanthrolin-5-amine (APhen). The microcontainers can respond to pH variation to release APhen which serves not only as a corrosion indicator but also as an inhibitor. An epoxy coating doped with 5 wt% microcontainers exhibited improved corrosion performance and was capable of inhibiting corrosion spreading from the damaged area in a 3.5 wt% NaCl solution. The salt spray test showed that corrosion damage can be quickly detected by the appearance of a red color within 2 min.