Quantum mechanics

Algorithm

Materials science

Embedded system

Crystal structure

Computer science

Perovskite (structure)

Physics

Mathematics

Ionic radius

Alkali metal

Atom (system on chip)

Crystallography

Chemistry

Octahedron

Lanthanide

Physical Review Materials

We apply machine learning (ML) methods to a database of 390 experimentally reported $AB{\mathrm{O}}_{3}$ compounds to construct two statistical models that predict possible new perovskite materials and possible new cubic perovskites. The first ML model classified the 390 compounds into 254 perovskites and 136 that are not perovskites with a 90% average cross-validation (CV) accuracy; the second ML model further classified the perovskites into 22 known cubic perovskites and 232 known noncubic perovskites with a 94% average CV accuracy. We find that the most effective chemical descriptors affecting our classification include largely geometric constructs such as the $A$ and $B$ Shannon ionic radii, the tolerance and octahedral factors, the $A$-O and $B$-O bond length, and the $A$ and $B$ Villars' Mendeleev numbers. We then construct an additional list of $625AB{\mathrm{O}}_{3}$ compounds assembled from charge conserving combinations of $A$ and $B$ atoms absent from our list of known compounds. Then, using the two ML models constructed on the known compounds, we predict that 235 of the 625 exist in a perovskite structure with a confidence greater than 50% and among them that 20 exist in the cubic structure (albeit, the latter with only $\ensuremath{\sim}50%$ confidence). We find that the new perovskites are most likely to occur when the $A$ and $B$ atoms are a lanthanide or actinide, when the $A$ atom is an alkali, alkali earth, or late transition metal atom, or when the $B$ atom is a $p$-block atom. We also compare the ML findings with the density functional theory calculations and convex hull analyses in the Open Quantum Materials Database (OQMD), which predicts the $T=0$ K ground-state stability of all the $AB{\mathrm{O}}_{3}$ compounds. We find that OQMD predicts 186 of 254 of the perovskites in the experimental database to be thermodynamically stable within 100 meV/atom of the convex hull and predicts 87 of the 235 ML-predicted perovskite compounds to be thermodynamically stable within 100 meV/atom of the convex hull, including 6 of these to be in cubic structures. We suggest these 87 as the most promising candidates for future experimental synthesis of novel perovskites.

Predictions of new <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi mathvariant="italic">AB</mml:mi><mml:msub><mml:mi mathvariant="normal">O</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math> perovskite compounds by combining machine learning and density functional theory

Predictions of new ABO3 perovskite compounds by combining machine learning and density functional theory