Machine Learning in Screening High Performance Electrocatalysts for CO₂ Reduction

Converting CO₂ into carbon-based fuels is promising for relieving the greenhouse gas effect and the energy crisis. However, the selectivity and efficiency of current electrocatalysts for CO₂ reductions are still not satisfactory. In this paper, the development of machine learning methods in screening CO₂ reduction electrocatalysts over the recent years is reviewed. Through high-throughput calculation of some key descriptors such as adsorption energies, d-band center, and coordination number by well-constructed machine learning models, the catalytic activity, optimal composition, active sites, and CO₂ reduction reaction pathway over various possible materials can be predicted and understood. Machine learning is now realized as a fast and low-cost method to effectively explore high performance electrocatalysts for CO₂ reduction.