Machine-learning-accelerated screening of Heusler alloys for nitrogen reduction reaction with graph neural network

Developing efficient catalysts for nitrogen reduction reaction (NRR) is a meaningful yet challenging endeavor. Here, we employ machine learning to screen efficient Heusler alloy (X2YZ) catalysts towards NRR. We incorporate classification tasks into the graph neural network to distinguish between adsorbates and adsorption sites which improving the network’s ability to recognize adsorption configurations and enhance its predictive accuracy of adsorption energy simultaneously. Following training on an adsorption energy dataset of 6,000 density-functional theory calculations, our model can predict the adsorption energies of critical adsorbates (N2, NNH, NH, NH2, H) with a mean absolute error of 0.1 eV. Through a multi-criteria screening, we identified a series of Ru-based Heusler catalysts with low limiting potentials and the ability to suppress hydrogen evolution reactions. For example, Ru2HfTl exhibits a low limiting potential of −0.32 V. Statistical analysis reveals that the average d-electron of X and Y elements, along with the group number of Z element, can assess the catalyst activity of Heusler alloys. Furthermore, we discover that the unique geometric structure of four-fold hollow sites on the (110) surface of Heusler alloy can facilitate N2 activation and alter the potential determining step of NRR.