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- Title
Classification of ABO<sub>3</sub> perovskite solids: a machine learning study.
- Authors
Pilania, G.; Balachandran, P. V.; Gubernatis, J. E.; Lookman, T.
- Abstract
We explored the use of machine learning methods for classifying whether a particular ABO3 chemistry forms a perovskite or non-perovskite structured solid. Starting with three sets of feature pairs (the tolerance and octahedral factors, the A and B ionic radii relative to the radius of O, and the bond valence distances between the A and B ions from the O atoms), we used machine learning to create a hyper-dimensional partial dependency structure plot using all three feature pairs or any two of them. Doing so increased the accuracy of our predictions by 2-3 percentage points over using any one pair. We also included the Mendeleev numbers of the A and B atoms to this set of feature pairs. Doing this and using the capabilities of our machine learning algorithm, the gradient tree boosting classifier, enabled us to generate a new type of structure plot that has the simplicity of one based on using just the Mendeleev numbers, but with the added advantages of having a higher accuracy and providing a measure of likelihood of the predicted structure.
- Subjects
PEROVSKITE; MACHINE learning; VALENCE bonds
- Publication
Acta Crystallographica Section B: Structural Science, Crystal Engineering & Materials, 2015, Vol 71, Issue 5, p507
- ISSN
2052-5192
- Publication type
Article
- DOI
10.1107/S2052520615013979