High-throughput ab initio calculations and machine learning to discover SrFeO3-δ-based perovskites for chemical-looping applications

Design of high-performance oxygen carrier materials plays a crucial role in chemical-looping applications. Perovskite-type ABO3 oxides have received significant attention due to their high thermal and mechanical stability and high oxygen mobility. In this work, a promising oxygen carrier, SrFeO3-δ, is systematically substituted, and the subsequent oxygen carrier properties are explored using density functional theory calculations in combination with machine learning methods. We have studied the oxygen vacancy formation energies and Gibbs free energy for a wide range of partial cationic substitutions at the A- and/or B-sites, including in the ground state and at finite temperatures (100–1,200 K). The calculated results are verified by thermogravimetric cycling and oxygen-temperature-programmed desorption measurements. Predictive machine learning models are developed based upon the datasets to identify important chemical-property relationships and confirm a previously investigated system, Sr1-xCaxFe1-yNiyO3-δ, and a discovered system, Sr1-xBaxFe1-yCuyO3-δ, as potential high-performance chemical-looping materials.