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

Engineering and Technology

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

T. Liu, Z. Chen, et al.

This study, conducted by Tong Liu, Zhuoyao Chen, Jingzhi Yang, Lingwei Ma, Arjan Mol, and Dawei Zhang, unveils a groundbreaking machine learning workflow that accurately predicts the corrosion resistance of self-healing epoxy coatings. Through advanced modeling and optimization, the researchers established a formulation that not only exhibits remarkable durability but also showcases excellent self-healing properties, outperforming conventional coatings in rigorous testing.

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Playback language: English
Abstract
This study presents a machine learning workflow for predicting the corrosion resistance of a self-healing epoxy coating containing ZIF-8@Ca microfillers. The workflow used orthogonal Latin square methods to investigate the effects of four parameters on the coating's low impedance modulus. A random forest (RF) model was selected for active learning, achieving good prediction accuracy after five cycles. Bayesian optimization identified the best coating formulation. The resulting coating showed excellent self-healing and corrosion resistance, with minimal corrosion and adhesion loss after 60 days of neutral salt spray testing.
Publisher
npj Materials Degradation
Published On
Jan 19, 2024
Authors
Tong Liu, Zhuoyao Chen, Jingzhi Yang, Lingwei Ma, Arjan Mol, Dawei Zhang
Tags
machine learning
corrosion resistance
self-healing epoxy
ZIF-8@Ca microfillers
random forest model
Bayesian optimization
neutral salt spray testing

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