High-throughput Computational Study of Double Perovskite Oxides

Perovskite oxides have been intensively studied for decades due to the extraordinary variability of compositions and structures and their attractive applications in superconductivity, magnetoresistance, multiferroicity, catalysis, solid oxide fuel cells, and etc. Extending from single perovskite $AB$O$_3$ to double perovskite $A_2BB'$O$_6$ significantly increases the tunability towards the targeted physical and chemical properties. However, the number of possible compositions of double perovskite is prohibitively large to explore entirely experimentally. In this talk, we will present how to use a multi-step high-throughput computational method to screen $\sim$ 5000 compositions of double perovskite $A_2BB'$O$_6$ ($A$=Ca, Sr, Ba, and La; $B$ and $B'$ are metal elements). With the $\sim$ 2000 stable/metastable (and so likely synthesizable) compounds predicted by our calculations, we will show how statistical learning of the large dataset can capture the correlation among composition, stability, and crystal structure (i.e., cubic perovskite, distorted perovskite, and non-perovskite). The results will accelerate new double perovskite oxides prediction and discovery.