Local Ordering, Distortion, and Redox Activity in (La0.75Sr0.25)(Mn0.25Fe0.25Co0.25Al0.25)O3 Investigated by a Computational Workflow for Compositionally Complex Perovskite Oxides.

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 (VO), 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 VO. Co is found to be the redox-active element and the VO is the preferentially generated next to Co due to the stretched Co-O bonds. A clear definition of the vacancy formation energy (Evf) is proposed for CCPO in an ensemble of structures evolved in parallel from independent DFT-MC paths. By combining the distribution of Evf with VO 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.

Perovskite Oxide Materials for Solar Thermochemical Hydrogen Production from Water Splitting through Chemical Looping.

Solar-driven thermochemical hydrogen (STCH) production represents a sustainable approach for converting solar energy into hydrogen (H2) as a clean fuel. This technology serves as a crucial feedstock for synthetic fuel production, aligning with the principles of sustainable energy. The efficiency of the conversion process relies on the meticulous tuning of the properties of active materials, mostly commonly perovskite and fluorite oxides. This Review conducts a comprehensive review encompassing experimental, computational, and thermodynamic and kinetic property studies, primarily assessing the utilization of perovskite oxides in two-step thermochemical reactions and identifying essential attributes for future research endeavors. Furthermore, this Review delves into the application of machine learning (ML) and density functional theory (DFT) for predicting and classifying the thermochemical properties of perovskite materials. Through the integration of experimental investigations, computational modeling, and ML methodologies, this Review aspires to expedite the screening and optimization of perovskite oxides, thus enhancing the efficiency of STCH processes. The overarching objective is to propel the advancement and practical integration of STCH systems, contributing significantly to the realization of a sustainable and carbon-neutral energy landscape.