Advanced Multifunctional Electrocatalysts: Integrating DFT and Machine Learning for OER, HER, and ORR Reactions

Expanding MXene applications in energy conversion and storage offers a promising approach to developing robust, multifunctional electrocatalysts. Progress in electrochemical energy systems is strongly dependent on effective catalysts for the oxygen evolution reaction (OER), hydrogen evolution reaction (HER), and oxygen reduction reaction (ORR). In this study, we used density functional theory (DFT) to investigate transition-metal-based single-atom catalysts (TM_SA) supported on Mo₂CS₂ MXene. Our findings revealed that the bifunctional overpotential for Ni_SA is 0.44 V for water splitting and 1.11 V for metal–air batteries, showcasing excellent catalytic performance. Volcano plots, based on Gibbs free energy changes for the intermediates OH*, O*, and OOH*, density of states and crystal orbital Hamilton population (COHP) effectively illustrate these results. Additionally, we utilized a multitask machine learning (MTL) approach to predict overpotentials for OER + HER and OER + ORR in the context of water splitting and metal–air batteries, respectively. Using the Sure Independence Screening and Sparsifying Operator (SISSO) method, we identified meaningful descriptors associated with catalytic activity. The key features influencing the adsorption behavior were found to include the shift of the d-band center and the difference in Bader charge upon the adsorption of O* and OH* on the TM_SA–MXene interface. This comprehensive study underscores the significant potential of Mo₂CS₂–Ni_SA as multifunctional electrocatalysts and offers crucial theoretical insights for the development of advanced catalysts capable of facilitating OER, ORR, and HER.