From Prediction to Materials Design: Machine Learning in Electrocatalytic Water Splitting

Key Points

• This research aims to explore how machine learning can improve the discovery of electrocatalysts for water splitting.
• Utilized machine learning techniques to analyze electrocatalysts
• Assessed effects of data quality on predictions
• Investigated thermodynamic bias in catalyst performance
• Identified key factors affecting electrocatalyst efficiency
• Demonstrated improved prediction accuracy with enhanced data quality
• Revealed limitations in current approaches due to thermodynamic biases