Topic: Advances in Machine Learning and AI for Designing Electrochemical Degradation-Resistant Materials

Electrochemical degradation, whether in marine alloys or water splitting catalysts, limits the potential of otherwise promising materials. Designing materials to resist degradation involves multi-objective optimization, balancing material performance (e.g., overpotential for oxygen evolution) with longevity. The vast search space, including high entropy alloys and novel oxides, poses significant challenges. Recent advances in high-throughput experimentation, machine learning (ML) and artificial intelligence (AI) have aided material exploration, but limited secondary characterization and existing descriptors often fall short for comprehensive material discovery. AI-controlled self-driving labs have shown promise but, to date, are mostly proof of concept studies rather than delivering robust material solutions. Additionally, current studies often focus on short-term degradation, while real-world applications require long-term durability.

This Special Issue will focus on using ML to address challenges in designing materials that resist electrochemical degradation. We invite original research and reviews on topics including:

1. Novel high-throughput experimental and computational workflows to investigate novel alloys classes;

2. Rich material representations for ML models predicting degradation;

3. ML and AI tools for forecasting long-term degradation;

4. Supervised and unsupervised methods for analyzing degradation data, such as electrochemical impedance and X-ray photoelectron spectroscopy;

5. ML tools for deriving physical mechanisms from electrochemical degradation studies.