Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction.

Area of Science:

• Materials Science
• Computational Chemistry
• Electrochemistry

Background:

• Efficient electrocatalysts are vital for water electrolysis-driven hydrogen production.
• Traditional "trial and error" methods for electrocatalyst development are inefficient and costly.
• Machine learning (ML) offers a promising alternative for accelerating electrocatalyst discovery and design.

Purpose of the Study:

• To review recent advancements in applying ML to low-dimensional electrocatalysts for hydrogen evolution reaction (HER) performance prediction.
• To highlight the impact of descriptors and algorithms in screening and evaluating electrocatalysts.
• To discuss future perspectives for ML in electrocatalysis.

Main Methods:

• Review of existing literature on ML applications in electrocatalysis.
• Analysis of ML models used for predicting HER performance of various low-dimensional materials (0D, 1D, 2D).
• Discussion of descriptor selection and algorithm choices in ML-driven electrocatalyst screening.

Main Results:

• ML effectively predicts the HER performance of electrocatalysts by analyzing experimental and theoretical data.
• Significant progress has been made in applying ML to diverse low-dimensional electrocatalyst structures.
• The choice of descriptors and algorithms critically influences the accuracy and efficiency of ML screening.

Conclusions:

• ML holds immense potential to revolutionize electrocatalyst discovery and design for hydrogen generation.
• ML can accelerate the optimization of electrocatalyst performance and provide deeper mechanistic insights.
• This review provides a comprehensive overview of ML in electrocatalysis, guiding future research directions.