Screening stable and metastable ABO3 perovskites using machine learning and the materials project
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Abstract
Machine learning and Materials Project are used to investigate stable and metastable perovskite materials based on a dataset of 397 ABO3 compounds. The best performance classification model Gradient Boosting Decision Tree (GBDT) can classify 397 compounds into 143 non-perovskites and 254 perovskites with a 94.6% accuracy over 10-fold cross-validation, which indicates that 9 descriptors are outstanding features for formability of perovskite: tolerance factor, octahedral factor, radius ratio of A to O, A-O and B-O bond length, electronegativity difference for A-O (B-O) multiplied by the radius ratio of A (B) to O, the Mendeleev numbers for A and B. Among 891 ABO3, the GBDT model predicts that 331 have perovskite structure and the top-174 within a probability ≥ 85%. Furthermore, based on the energy above the convex hull (Ehull), 37 thermodynamically stable ABO3 perovskites with 0≤Ehull<36meV/atom and 13 metastable perovskites with 36≤Ehull<70meV/atom are predicted for further synthesis and applications.