Palladium (Pd) hydride-based catalysts have been reported
to have
excellent performance in the CO2 reduction reaction (CO2RR) and hydrogen evolution reaction (HER). Our previous work
on doped PdH and Pd alloy hydrides showed that Ti-doped and Ti-alloyed
Pd hydrides could improve the performance of the CO2 reduction
reaction compared with pure Pd hydride. Compositions and chemical
orderings of the surfaces with only one adsorbate under certain reaction
conditions are linked to their stability, activity, and selectivity
toward the CO2RR and HER, as shown in our previous work.
In fact, various coverages, types, and mixtures of the adsorbates,
as well as state variables such as temperature, pressure, applied
potential, and chemical potential, could impact their stability, activity,
and selectivity. However, these factors are usually fixed at common
values to reduce the complexity of the structures and the complexity
of the reaction conditions in most theoretical work. To address the
complexities above and the huge search space, we apply a deep learning-assisted
multitasking genetic algorithm to screen for Pd
x
Ti1–x
H
y
surfaces containing multiple adsorbates for CO2RR under different reaction conditions. The ensemble deep learning
model can greatly speed up the structure relaxations and retain a
high accuracy and low uncertainty of the energy and forces. The multitasking
genetic algorithm simultaneously finds globally stable surface structures
under each reaction condition. Finally, 23 stable structures are screened
out under different reaction conditions. Among these, Pd0.56Ti0.44H1.06 + 25%CO, Pd0.31Ti0.69H1.25 + 50%CO, Pd0.31Ti0.69H1.25 + 25%CO, and Pd0.88Ti0.12H1.06 + 25%CO are found to be very active for CO2RR and suitable to generate syngas consisting of CO and H2.