Computational screening of M2N4-C-type dual-atom-catalysts for electrochemical ammonia synthesis by the first-principles DFT and machine learning

Abstract

Electrochemical ammonia synthesis is expected to complement the conventional Haber-Bosch method due to its low carbon emissions and stable operation under ambient conditions. However, due to the complexity of reaction pathways in nitrogen reduction reaction (NRR), rapid identification and prediction of NRR electrocatalyst is computationally expensive and challenging. In this work, taking the graphene-based M2N4-C dual-atom-catalysts (DACs) family as an example, we investigated the NRR activity and mechanisms on 45 candidates with the M from 3d transition metals. Six candidates were predicted to be promising NRR catalysts from DFT calculation. A universal descriptor Ф is trained from 4860 DFT-obtained data points to predict NRR activity and path preference. The ML-trained descriptor Ф shows 84% probability in correctly qualitative prediction of NRR activity. Most importantly, the robustness and transferability of descriptor Ф is further confirmed in other M2N4-C DACs with M in 4d transition metals. Our study shows a practical strategy for fast computational screening of NRR catalysts based on DFT and ML-trained universal descriptor, which could significantly benefit the development of electrochemical ammonia synthesis in industry.