  The catalyst development for N2 electroreduction reaction (NRR) with low
onset potential and high Faradaic efficiency is highly desired, but remains
challenging. Machine learning (ML) recently emerged as a complementary tool to
accelerate material discovery; however a ML model for NRR has yet to be
developed. Here, we develop and report slab-graph convolutional neural network
(SGCNN), an accurate and flexible ML model that is applicable to catalytic
surface reactions. With the self-accumulated database of 2,699 surface
calculations, SGCNN predict binding energies, ranging over 8 eV, of five key
adsorbates (*H, *N2, *N2H, *NH, *NH2) related to NRR performance with the
mean-absolute-error of only 0.23eV. Unlike previously available models, SGCNN
avoids using ab initio level inputs, instead is solely based on elemental
properties that are all readily available in Periodic-Table-of-Elements; true
accelerations can be realized. t-distributed stochastic neighbor embedding
(t-SNE) analysis reveals that binary intermetallics of averaged d-electron
occupation between 4 and 5 could potentially lower the onset potential in N2
electroreduction.
