Abstract: Developing machine learning frameworks with predictive power, interpretability, and transferability is crucial, yet it faces challenges in the field of electrocatalysis. To achieve this, we employed rigorous feature engineering to establish a finely tuned gradient boosting regressor (GBR) model, which adeptly captures the physical complexity from feature space to target variables. We demonstrated that environmental electron effects and atomic number significantly govern the success of the mapping process via global and local explanations. The finely tuned GBR model exhibits exceptional robustness in predicting CO adsorption energies (R_ave² = 0.937, RMSE = 0.153 eV). Moreover, the model demonstrated remarkable transfer learning ability, showing excellent predictive power for OH, NO, and N₂ adsorption. Importantly, the GBR model exhibits exceptional predictive capability across an extensive search space, thereby demonstrating profound adaptability and versatility. Our research framework significantly enhances the interpretability and transferability of machine learning in electrocatalysis, offering vital insights for further advancements.

Key words: Machine learning, First-principles calculation, Interpretability, Transferability, CO reduction