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Title: Classification of AB O 3 perovskite solids: a machine learning study

Here we explored the use of machine learning methods for classifying whether a particularABO3chemistry forms a perovskite or non-perovskite structured solid. Starting with three sets of feature pairs (the tolerance and octahedral factors, theAandBionic radii relative to the radius of O, and the bond valence distances between theAandBions 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 theAandBatoms to this set of feature pairs. Moreover, 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:
 [1] ;  [1] ;  [1] ;  [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
OSTI Identifier:
1257974
Report Number(s):
LA-UR--15-22854
Journal ID: ISSN 2052-5206; ACSBDA; PII: S2052520615013979
Grant/Contract Number:
AC52-06NA25396
Type:
Accepted Manuscript
Journal Name:
Acta Crystallographica. Section B, Structural Science, Crystal Engineering and Materials (Online)
Additional Journal Information:
Journal Name: Acta Crystallographica. Section B, Structural Science, Crystal Engineering and Materials (Online); Journal Volume: 71; Journal Issue: 5; Journal ID: ISSN 2052-5206
Publisher:
International Union of Crystallography
Research Org:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE