Perovskite oxides
are attractive candidates for various scientific
applications because of their outstanding structure flexibilities
and attractive physical and chemical properties. However, labor-intensive
and high-cost experimental and density functional theory calculation
approaches are normally used to screen candidate perovskites. Herein,
a machine learning method is employed to identify perovskites from
ABO
3
combinations formulated as constraint satisfaction
problems based on the restrictions of charge neutrality and Goldschmidt
tolerance factor. By eliminating five features based on their correlation
and importance, 16 features refined from 21 features are employed
to describe 343 known ABO
3
compounds for perovskite formability
and stability model training. It is found that the top three features
for predicting formability are structural features of the A–O
bond length, tolerance, and octahedral factors, whereas the top nine
features for predicting the stability are elemental and structural
features related to the B-site elements. The precision and recall
of the two models are 0.983, 1.00 and 0.971, 0.943, respectively.
The formability prediction model categorizes 2229 ABO
3
combinations
into 1373 perovskites and 856 nonperovskites, whereas the stability
prediction model distinguishes 430 stable perovskites from 1799 unstable
ones. Three hundred thirty-eight combinations are recognized as both
formable and stable perovskites for future investigation.