title: Classification of ABO3 perovskite solids: a machine learning study.
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.
authors.affiliation: Materials Science and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA.
authors.forename: G
authors.initials: G
authors.lastname: Pilania
authors.affiliation: Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA.
authors.forename: P V
authors.initials: PV
authors.lastname: Balachandran
authors.affiliation: Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA.
authors.forename: J E
authors.initials: JE
authors.lastname: Gubernatis
authors.affiliation: Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA.
authors.forename: T
authors.initials: T
authors.lastname: Lookman
keywords: bond valence; gradient tree boosting classifier; machine learning study; perovskites