2026-10-15T02:03:55Z https://www.scidb.cn/oai

10.57760/sciencedb.45674 2026-09-20T10:10:22Z

2026-09-20 Machine Learning-Guided Catalyst Design for Ethane–CO₂ Co-Conversion doi:10.57760/sciencedb.45674 en CO2 oxidative dehydrogenation of ethane (CO2-ODHE) couples ethane upgrading with CO2 utilization, but reliable catalyst screening remains hindered by cross-literature heterogeneity and publication-specific data leakage. Here, 511 records from 59 publications were curated into 509 canonical entries, including 504 valid C2H4 yields. Six machine learning algorithms were evaluated using fold-contained preprocessing, DOI-grouped validation, chronological validation, and 50 repeated DOI-stratified holdouts. Gradient Boosting and XGBoost showed the most consistent cross-literature performance, with mean repeated-holdout R2 values of 0.539 ± 0.115 and 0.544 ± 0.105, respectively. Convex stacking reduced variability but did not improve mean predictive accuracy. Subgroup analysis revealed weaker transfer to non-Cr catalysts. SHAP identified reaction temperature, BET surface area, and CO2 partial pressure as the dominant predictors. Applicability-domain analysis was further used to prioritize candidates for prospective validation, providing a more realistic framework for machine-learning-assisted CO2-ODHE catalyst screening. CO2 oxidative dehydrogenation of ethane; Machine learning; Publication-aware validation; SHAP interpretability; Catalyst screening. 石梓涵 郑安庆 RESTRICTED dataset Science Data Bank