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

Pd-Ti/Nb alloy hydrides: An active learning cluster expansion (ALCE) surrogate model equipped with Monte Carlo simulated annealing (MCSA), a CO* binding energy filter and a kinetic model is used to identify promising Pd _x Ti_1-x H _y and Pd _x Nb_1-x H _y catalysts with high stability and superior activity. Using our approach, we identify 24 stable and active candidates of Pd _x Ti_1-x H _y and 6 active candidates of Pd _x Nb_1-x H _y .

Abstract

Electrochemical 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 Pd _x Ti_1-x H _y and Pd _x Nb_1-x H _y 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 Pd _x M_1-x H _y 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 Pd _x Ti_1-x H _y and Pd _x Nb_1-x H _y catalysts with high stability and superior activity. Using our approach, we identify 24 stable and active candidates of Pd _x Ti_1-x H _y and 5 active candidates of Pd _x Nb_1-x H _y . Among these candidates, the Pd_0.23Ti_0.77H, Pd_0.19Ti_0.81H_0.94, and Pd_0.17Nb_0.83H_0.25 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.

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