In this work, an active protective epoxy coating with weathering resistant, corrosion-warning, and self-healing properties was developed by incorporating tannic acid (TA) loaded mesoporous silica (MSN-TA) nanocontainers. The introduction of MSN-TA nanocontainers could alleviate the coating degradation via scavenging the radicals generated during UV irradiation. Compared with the blank coating, the coating containing 5 wt.% MSN-TA nanocontainers exhibited much less degradation in surface morphology, wettability and glossiness, and maintained a good barrier property after 384 h of accelerated weathering. Once the coating was damaged, the released TA could react with the Fe³⁺ ions to form a chelate that endowed the coating scratch with a visible black coloration, i.e. triggering a self-warning capability to indicate the initial onset of corrosion. In addition, the generated chelate could inhibit extensive corrosion propagation, offering a significant self-healing effect demonstrated by the stabilized impedance modulus values during 28 days of immersion in NaCl solution.

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 (R²) = 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.