High‐throughput Compositional Screening of PdxTi1‐xHy and PdxNb1‐xHy Hydrides for CO2 Reduction**

Abstract

AbstractElectrochemical experiments and theoretical calculations have shown that Pd‐based metal hydrides can perform well for the CO2 reduction reaction (CO2RR). Our previous work on doped‐PdH showed that doping Ti and Nb into PdH can improve the CO2RR activity, suggesting that the Pd alloy hydrides with better performance are likely to be found in the PdxTi1‐xHy and PdxNb1‐xHy phase space. However, the vast compositional and structural space with different alloy hydride compositions and surface adsorbates, makes it intractable to screen out the stable and active PdxM1‐xHy catalysts using density functional theory calculations. Herein, an active learning cluster expansion (ALCE) surrogate model equipped with Monte Carlo simulated annealing (MCSA), a CO* binding energy filter and a kinetic model are used to identify promising PdxTi1‐xHy and PdxNb1‐xHy catalysts with high stability and superior activity. Using our approach, we identify 24 stable and active candidates of PdxTi1‐xHy and 5 active candidates of PdxNb1‐xHy. Among these candidates, the Pd0.23Ti0.77H, Pd0.19Ti0.81H0.94, and Pd0.17Nb0.83H0.25 are predicted to display current densities of approximately 5.1, 5.1 and 4.6 μA cm−2 at −0.5 V overpotential, respectively, which are significantly higher than that of PdH at 3.7 μA cm−2.