Abstract
Perovskite oxides (ternary chemical formula ABO(3)) are a diverse class of materials with applications including heterogeneous catalysis, solid-oxide fuel cells, thermochemical conversion, and oxygen transport membranes. However, their multicomponent (chemical formula [Formula: see text]) chemical space is underexplored due to the immense number of possible compositions. To expand the number of computed [Formula: see text] compounds we report a dataset of 66,516 theoretical multinary oxides, 59,708 of which are perovskites. First, 69,407 [Formula: see text] compositions were generated in the a(-)b(+)a(-) Glazer tilting mode using the computationally-inexpensive Structure Prediction and Diagnostic Software (SPuDS) program. Next, we optimized these structures with density functional theory (DFT) using parameters compatible with the Materials Project (MP) database. Our dataset contains these optimized structures and their formation (ΔH(f)) and decomposition enthalpies (ΔH(d)) computed relative to MP tabulated elemental references and competing phases, respectively. This dataset can be mined, used to train machine learning models, and rapidly and systematically expanded by optimizing more SPuDS-generated [Formula: see text] perovskite structures using MP-compatible DFT calculations.