Mixing multiple cations
can result in a significant configurational
entropy, offer a new compositional space with vast tunability, and
introduce new computational challenges. For applications such as the
two-step solar thermochemical hydrogen (STCH) generation techniques,
we demonstrate that using density functional theory (DFT) combined
with Metropolis Monte Carlo method (DFT-MC) can efficiently sample
the possible cation configurations in compositionally complex perovskite
oxide (CCPO) materials, with (La0.75Sr0.25)(Mn0.25Fe0.25Co0.25Al0.25)O3 as an example. In the presence of oxygen vacancies (V
O), DFT-MC simulations reveal a significant
increase of the local site preference of the cations (short-range
ordering), compared to a more random mixing without V
O. Co is found to be the redox-active element and the V
O is the preferentially generated next to Co
due to the stretched Co–O bonds. A clear definition of the
vacancy formation energy (E
v
f) is proposed for CCPO in an ensemble
of structures evolved in parallel from independent DFT-MC paths. By
combining the distribution of E
v
f with V
O interactions into a statistical model, the oxygen nonstoichiometry
(δ), under the STCH thermal reduction and oxidation conditions,
is predicted and compared with the experiments. Similar to the experiments,
the predicted δ can be used to extract the enthalpy and entropy
of reduction using the van’t Hoff method, providing direct
comparisons with the experimental results. This procedure provides
a full predictive workflow for using DFT-MC to obtain possible local
ordering or fully random structures, understand the redox activity
of each element, and predict the thermodynamic properties of CCPOs,
for computational screening and design of these CCPO materials at
STCH conditions.