Accelerating the design of catalysts for CO2 electroreduction to HCOOH: A data-driven DFT-ML screening of dual atom catalysts

Abstract

Dual-atom catalysts (DACs) have emerged as potential catalysts for effective electroreduction of CO2 due to their high atom utilization efficiency and multiple active sites. However, the screening of DACs remains a challenge due to the large number of possible combinations, making exhaustive experimental or computational screening a daunting task. In this study, a density functional theory (DFT)-based machine learning (ML)-accelerated (DFT-ML) hybrid approach was developed to test a set of 406 dual transition metal catalysts on N-doped graphene (NG) for the electroreduction of CO2 to HCOOH. The results showed that the ML algorithms can successfully capture the relationship between the descriptors of the DACs (inputs) and the limiting potential for HCOOH generation (output). Of the four ML algorithms studied in this work, the feedforward neural network model achieved the highest prediction accuracy (the highest correlation coefficient (R2) of 0.960 and the lowest root mean square error (RMSE) of 0.319 eV on the test set) and the predicted results were verified by DFT calculations with an average absolute error of 0.14 eV. The DFT-ML approach identified Co-Co-NG and Ir-Fe-NG as the most active and stable electrocatalysts for the electrochemical reduction of CO2 to HCOOH. The DFT-ML hybrid approach exhibits exceptional prediction accuracy while enabling a significant reduction in screening time by an impressive 64% compared to conventional DFT-only calculations. These results demonstrate the immense potential of using ML methods to accelerate the screening and rational design of efficient catalysts for various energy and environmental applications.