The Development of New Perovskite-Type Oxygen Transport Membranes Using Machine Learning

The aim of this work is to predict suitable chemical compositions for the development of new ceramic oxygen gas separation membranes, avoiding doping with toxic cobalt or expensive rare earths. For this purpose, we have chosen the system Sr<INF>1-x</INF>Ba<INF>x</INF>(Ti<INF>1-y-z</INF>V<INF>y</INF>Fe<INF>z</INF>)O<INF>3-delta</INF> (cubic perovskite-type phases). We have evaluated available experimental data, determined missing crystallographic information using bond-valence modeling and programmed a Python code to be able to generate training data sets for property predictions using machine learning. Indeed, suitable compositions of cubic perovskite-type phases can be predicted in this way, allowing for larger electronic conductivities of up to sigma<INF>e</INF> = 1.6 S/cm and oxygen conductivities of up to sigma<INF>i</INF> = 0.008 S/cm at T = 1173 K and an oxygen partial pressure p<INF>O<INF>2</INF></INF> = 10-15 bar, thus enabling practical applications.