Accurate and efficient machine learning models for predicting hydrogen evolution reaction catalysts based on
Jingzi Zhang1,2, Yuelin Wang1,2, Xuyan Zhou1,2
1School of Materials Science and Engineering, Harbin Institute of Technology, Shenzhen 518055, Guangdong, China. linxi@hit.edu.cn.
Nanoscale
|June 19, 2023
Summary
Machine learning models accurately predict high-performance alloy electrocatalysts for hydrogen production via water electrolysis. This accelerates the discovery of novel materials for the hydrogen evolution reaction (HER).
Area of Science:
- Materials Science
- Electrochemistry
- Computational Chemistry
Background:
- Designing high-performance alloy electrocatalysts for hydrogen production via water electrolysis is a significant challenge.
- The vast number of possible alloy compositions makes experimental and computational screening difficult.
Purpose of the Study:
- To develop accurate and efficient machine learning (ML) models for predictive materials design of alloy electrocatalysts.
- To identify promising alloy candidates for the hydrogen evolution reaction (HER).
Main Methods:
- Incorporated electronic and structural properties of alloys into ML models.
- Utilized the light gradient boosting (LGB) algorithm for prediction.
- Estimated feature importance for predicting Gibbs free energy of hydrogen adsorption (ΔGH*).
Main Results:
- Achieved a high coefficient of determination (R2 = 0.921) and low root-mean-square error (RMSE = 0.224 eV) using the LGB model.
- Identified electronic and structural adsorption site features as critical for predicting ΔGH*.
- Screened 84 potential alloys with |ΔGH*| < 0.1 eV from 2290 candidates.
Conclusions:
- ML models incorporating structural and electronic features can accelerate the discovery of efficient electrocatalysts.
- This approach offers new insights for developing catalysts for HER and other heterogeneous reactions.
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