Unlocking the Nitrogen Reduction Electrocatalyst with a Dual-Metal-Boron System: From High-Throughput Screening to Machine Learning.

Recently, dual-metal catalysts have attracted much attention due to their abundant active sites and tunable chemical properties. On the other hand, metal borides have been widely applied in splitting the inert chemical bonds in small molecules (such as N) because of their excellent catalytic performances. As a combination of the above two systems, in this work, 11 kinds of transition metal atoms (TM = Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Mo, and W) were selected to embed in boron-doped graphene (BG) to construct 66 dual-metal-boron systems, and their performances toward the N reduction reaction (NRR) were examined using first-principles simulations. Our results revealed that such a dual-TM@BG system exhibits excellent thermodynamic and electrochemical stabilities, which facilitate the experimental synthesis. In particular, Fe-Fe- and Fe-Co-doped BG exhibit excellent performance for NRR, with the limiting potentials of -0.29 and -0.32 V, respectively, and both of them exhibit inhibitory effects on the H evolution reaction. Remarkably, the microkinetic modeling analysis revealed that the turnover frequency for the NH production on FeFe@BG reaches up to 7.27 × 10 s site at 700 K and 100 bar, which further confirms its ultrafast reaction rate. In addition, the machine learning method was employed to further understand the catalytic mechanism, and it is found that the NRR performances of dual-TM@BG catalysts are closely related to the sum of radii of two TM atoms. Therefore, our work not only proposed two promising electrocatalysts for NRR but also verified the feasibility for the application of a dual-metal-boron system in NRR.