Speaker
Description
I present a machine learning framework to investigate the catalytic activity of monolayer binary alloys toward the oxygen reduction reaction (ORR). Leveraging a dataset comprising thousands of density functional theory (DFT) calculations of OH adsorption energies on AgPt/Pt(111), AuCu/Cu(111), AuPt/Pt(111), and AuPd/Pd(111) monolayer alloy surfaces, I engineered 25 structural, energetic, and compositional features to capture complex physicochemical interactions. Tree-based models were developed to predict adsorption energies of ORR intermediates and to perform two classification tasks: (i) identifying the adsorption site (Au, Cu, Pd, or Pt), and (ii) classifying adsorption states as compressed or expanded. Both classification models achieved high accuracy, demonstrating robust performance. The adsorption energy of OH on monolayer surfaces was predicted through supervised regression using LightGBM and XGBoost models. Through cross-validation and hyperparameter tuning, model interpretability was enhanced using feature importance and SHAP analyses. Notably, despite comprehensive feature engineering, the lattice parameter of the guest emerged as an important predictive descriptor. This finding aligns with the established critical role of the lattice parameter in oxygen reduction reaction (ORR) activity.[[1],2] Overall, these findings illustrate how targeted feature engineering, coupled with interpretable machine learning, can identify key physicochemical descriptors and expedite the data-driven discovery of efficient ORR catalysts
References
1 Ozório, MS, et al., Journal of Catalysis, 443, 2025, 115988
2 Ozório, MS, et al., Journal of Catalysis, 433, 2024, 115484
| Broad physics domain | Condensed matter |
|---|---|
| AI/ML technique(s) to be presented | LightGBM, XGBoost, Supervised regression, Classification tasks |
