Abstract
We apply machine learning (ML) methods to a database of 390 experimentally reported ABO3 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 625ABO3 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 ∼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 ABO3 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.
| Original language | English (US) |
|---|---|
| Article number | 043802 |
| Journal | Physical Review Materials |
| Volume | 2 |
| Issue number | 4 |
| DOIs | |
| State | Published - Apr 11 2018 |
Funding
This work of P.V.B., J.E.G., and T.L. was supported in part by the Laboratory Directed Research and Development program of the Los Alamos National Laboratory. A.A.E. and C.W. (High-throughput DFT calculations) were supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Grant DE-FG02-07ER46433. A.Z. (analysis of the ML vs DFT results) is supported by the US Department of Energy, Office of Science, Basic Energy Science, MSE Division under Grant No. DE-FG02-13ER46959 to CU Boulder. We thank J. Lashley for a helpful conversation. The machine learning calculations were performed by P.V.B., J.E.G., and T.L. The phonon calculations were performed by P.V.B., C.W., and A.A.E. extracted the information from OQMD, and A.A.E. performed additional DFT calculations. All authors participated in the analysis and interpretation of the results.
ASJC Scopus subject areas
- General Materials Science
- Physics and Astronomy (miscellaneous)