An auto‐configurable machine learning framework to optimize and predict catalysts for CO₂ to light olefins process

Abstract

This study proposed an auto‐configurable machine learning framework based on the differential evolution algorithm (AutoML‐DE) driven by hybrid data for the screening and discovery of promising CO₂ to light olefins (CO₂TLO) catalysts candidates. The hybrid dataset comprises 532 experimental data from the literature and 296 simulation data. Results show that the AutoML‐DE model with extreme gradient boosting algorithms demonstrated superior performance for predicting the conversion ratio of CO₂ and selectivity of light olefins (average R² > 0.86). After identifying the input feature with the most significant impact on the output feature, the optimal AutoML‐DE model integrated with the genetic algorithm is applied to multiobjective optimization, sensitivity analysis, and prediction of new CO₂TLO catalysts. The optimized Cu‐Zn‐Al/SAPO‐34 catalyst has the highest catalytic performance among the reported CO₂TLO catalysts. Moreover, five new CO₂TLO catalysts with higher yields are successfully predicted. However, the performance of these catalysts should be further verified by experiment.