Machine learning assisted discovery of high-efficiency self-healing epoxy coating for corrosion protection

Abstract

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.