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

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_xTi_1-xH_y and Pd_xNb_1-xH_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_xM_1-xH_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_xTi_1-xH_y and Pd_xNb_1-xH_y catalysts with high stability and superior activity. Using our approach, we identify 24 stable and active candidates of Pd_xTi_1-xH_y and 5 active candidates of Pd_xNb_1-xH_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.

Keywords: CO2 reduction; PdxM1-xHy; active learning; cluster expansion; complex hydrides; high-throughput screening; kinetic activity; selectivity.