(559a) Inverse Design of Perovskite Materials through the Crossfit CEF and Bayesian Data Selection Algorithms for Solar Thermochemical Hydrogen Production

The thermodynamic properties of metal oxides as a function of off-stoichiometry are crucial materials design attributes in metal oxide-based oxygen-exchange chemical processes. It has been shown that the compound energy formalism (CEF) is a powerful framework to describe these thermodynamic properties. Recently, we developed the CrossFit CEF method for fitting the CEF model that overcomes the current fitting challenges associated with non-unique fits in part, due to the thousands of possible liner combinations of the parameters involved resulting in dynamic behavior between enthalpy and entropy trends. The key innovations are: 1) the combination of density functional calculations with experimental data that delineates the enthalpic/entropic contributions to the Gibbs free energy; 2) a systematic determination of the important CEF model terms, removing thermodynamic predetermining human intervention; 3) a self-consistent solution of the starting oxygen off-stoichiometry (δ₀) of thermogravimetric measurements. With the advent of this state-of-the-art algorithm our method enables the reliable extraction of off-stoichiometric metal oxide thermodynamic properties and facilitates rapid materials compositional screening, and reliable process design of systems dependent on off-stoichiometric redox-active metal oxides. We build on this concept by examining the use of a Bayesian approach to selecting data points which should be gathered next. To date, this active data selection technique has not been applied or refined for thermodynamic characterization of metal oxide reduction/re-oxidation cycle materials. We focus our study on the investigation of perovskite (ABO_3-δ) metal oxides for solar thermochemical water separation (STCH). The combination of the CrossFit CEF and Bayesian methods provides more reliable thermodynamic characterization and identifies, a priori, critical points to examine in the temperature, O₂ partial pressure, compositional landscape, thus minimizing the number of points which must be examined. Specifically, we show that a model identical to a ground truth model can be realized with half the data from careful selection of data points through the Bayesian approach. Furthermore, we show that the Bayesian informed approach does better than pure random sampling of data. We then couple the robust thermodynamic models to a STCH system level elucidating high efficiency materials at various operating conditions. Likewise, in an inverse design process, hypothetical materials can be inferred from the STCH system level model and in a high throughput process with the CrossFit CEF and Bayesian data selection algorithms a material can be identified to match the STCH system modeled material. Overall, implementation of our method will save significant time in data collection, allowing for more materials to be investigated and lower research costs. Further, it opens the possibility of a hands-off high-throughput process for metal oxide material selection and design and while not yet directly interfaced with TGA or other data acquisition software, these algorithms could easily be integrated for seamless informed data collection by TGA where a thermodynamic model is built in real time as the TGA collects more data.