Electroreduction of nitrate to ammonia on graphyne-based single-atom catalysts by combined density functional theory and machine learning study

Abstract

Nitrate pollution has emerged as a significant global issue, prompting increased interests in electrocatalytic reduction of nitrate to ammonia (NO3RR). This study explores the use of graphyne as the substrate to anchor single transition metal (TM) atoms from the 3d and 4d series on acetylene corners, creating single-atom catalysts (SAC) for the NO3RR reaction. Two catalysts, CrGY and MoGY, were finally identified as promising catalysts for NO3RR with low limiting potential and high selectivity for NH3. To further investigate the origin of catalyst activity, in addition to traditional electronic structrue analysis, machine learning models of the least solution shrinkage and selection operator (LASSO), the sure independence screening and sparsifying operator (SISSO) and the gradient boosting regression (GBR) algorithm are adopt to reveal the intrinsic properties of catalysts that determine the NO3RR performance. After preliminary evaluation of the 13 prepared features with LASSO algorithm, GBR models revealed that the nitrate adsorption free energy ΔGNO3- and the NO bond length are the two most important descriptors for NO3RR performance. A general equation for the relationship between the limiting potential and the characteristics of the catalysts are obtained by SISSO model. Finally, with the assitance of combined density functional calculations and machine learning, this study provides new enlightening insights into practical application of graphyne as SACs for NO3RR.