While solar energy is the most abundant renewable energy resource, the capture, storage, and distribution of it remains a challenge. Solar thermochemical water splitting (STWS) provides a promising route for efficient utilization of this disperse resource since it allows for use of the entire solar spectrum to convert water to an energy dense fuel, H<sub>2</sub>. However, despite a significant number of materials having been examined, an optimal redox material to drive this process has yet to be developed. In order to be economically viable, a material must have a high hydrogen production capacity, operate at acceptably low temperatures (&lt;1500°C), have fast kinetics, and be stable over many reduction/oxidation cycles. In this work we use a computationally accelerated and experimentally validated approach with advanced machine learned models to accelerate the discovery of new, durable perovskite and spinel metal oxides for STWS. Accurate modeling of materials using density functional theory (DFT) is particularly challenging for high temperature applications. We apply DFT, incorporating the effects of structural changes, magnetic disorder, and atomic disorder present in complex metal oxides, to accurately predict solar thermal water splitting behavior of spinels and perovskites at relevant operating conditions.  We also develop methods to incorporate effects beyond the dilute defect limit and predict the temperature dependence of oxygen vacancy concentration in these types of metal oxides. Experimental results from thermogravimetric analysis and redox cycling in a stagnation flow reactor are presented to validate DFT models and on-sun hydrogen production demonstrates the scalability of this process.

<i>Ab-initio</i> modeling and experimental demonstration of metal oxides for solar thermochemical water splitting

255th National Meeting (2018)

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Ab-initio modeling and experimental demonstration of metal oxides for solar thermochemical water splitting