Session: 17-06: Symposium Steinfeld - Radiative and materials characterization and solar technology development

Paper Number: 142246

142246 - A Bayesian Approach to a Priori Data Point Collection for Thermodynamic Modeling of Off-Stoichiometric Metal Oxides 

Abstract: 

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 a 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. This work aims to fill that methodological gap through a novel algorithm which systematically selects the most important experimental data points to collect without prior knowledge of the system behavior. Data points to examine include measuring the non-stoichiometric at a particular temperature and pressure in an experimental dataset and new theoretically calculated points, i.e. the energy of a particular system composition and off-stoichiometry. The combination of these 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. 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, this algorithm 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.

 

Presenting Author: Steven Wilson ASU

Presenting Author Biography: Steven Wilson is a Post-Doc at Arizona State University. After serving 10 years in the US Navy working in the Navy Nuclear Propulsion Program, he separated from the Navy and pursued his bachelor’s degree and PhD in chemical engineering from Arizona State University under the advisership of Chris Muhich. In 2021 he was awarded the prestigious 4-year DOE Computational Science Graduate Fellowship (CSGF). Steven graduated from ASU in the Spring of 2024. He specializes in the use of use of computational chemistry and thermodynamic modeling to derive fundamental understanding of novel material generation in the renewable energy field.

Authors:

Steven Wilson ASU
Christopher Muhich Arizona State University