A prediction model for CO < sub > 2 < /sub > /CO adsorption performance on binary alloys based on machine learning

In this study, an ML model was developed to predict the CO2 and CO adsorption affinity on single-atom doped binary alloys based on the thermochemical properties of component metals. By using a greedy algorithm, the best combination of features was determined, and the ML model was trained and verified based on a data set containing 78 alloys on which the adsorption energy values of CO2 and CO were calculated from DFT. Comparison between predicted and DFT calculated adsorption energy values suggests that the extreme gradient boosting (XGBoost) algorithm has excellent generalization performance, and the R-squared (R2) for CO2 and CO adsorption energy prediction are 0.96 and 0.91, respectively. The errors of predicted adsorption energy are 0.138 eV and 0.075 eV for CO2 and CO, respectively. This model can be expected to advance our understanding of structure-property relationships at the fundamental level and be used in large-scale screening of alloy-based catalysts.PMID:38628487 | PMC:PMC11019484 | DOI:10.1039/d4ra00710g
Source: Adv Data - Category: Epidemiology Authors: Source Type: research