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Title: Classification of AB O3 perovskite solids: a machine learning study

Abstract

Here we explored the use of machine learning methods for classifying whether a particular ABO3chemistry 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. 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]
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  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1257974
Report Number(s):
LA-UR-15-22854
Journal ID: ISSN 2052-5206; ACSBDA; PII: S2052520615013979
Grant/Contract Number:  
AC52-06NA25396
Resource 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
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE

Citation Formats

Pilania, G., Balachandran, P. V., Gubernatis, J. E., and Lookman, T. Classification of AB O3 perovskite solids: a machine learning study. United States: N. p., 2015. Web. doi:10.1107/S2052520615013979.
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Pilania, G., Balachandran, P. V., Gubernatis, J. E., & Lookman, T. Classification of AB O3 perovskite solids: a machine learning study. United States. https://doi.org/10.1107/S2052520615013979
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Pilania, G., Balachandran, P. V., Gubernatis, J. E., and Lookman, T. Thu . "Classification of AB O3 perovskite solids: a machine learning study". United States. https://doi.org/10.1107/S2052520615013979. https://www.osti.gov/servlets/purl/1257974.
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@article{osti_1257974,
title = {Classification of AB O3 perovskite solids: a machine learning study},
author = {Pilania, G. and Balachandran, P. V. and Gubernatis, J. E. and Lookman, T.},
abstractNote = {Here we explored the use of machine learning methods for classifying whether a particular ABO3chemistry 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. 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.},
doi = {10.1107/S2052520615013979},
journal = {Acta Crystallographica. Section B, Structural Science, Crystal Engineering and Materials (Online)},
number = 5,
volume = 71,
place = {United States},
year = {Thu Jul 23 00:00:00 EDT 2015},
month = {Thu Jul 23 00:00:00 EDT 2015}
}
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https://doi.org/10.1107/S2052520615013979
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Cited by: 43 works
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Figures / Tables:

Figure 1 Figure 1: Crystal structures of (a) the perovskite class and (b)–(d) the nonperovskite class. (a) Perovskite with three-dimensional network of corner-sharing BO6 octahedral units (e.g. SrTiO3). (b) Hexagonal nonperovskite with face-sharing octahedral units (e.g. BaMnO3). (c) Ilmenite non-perovskite with edge-sharing octahedral units (e.g. FeTiO3). (d) Calcite non-perovskite with no octahedralmore » unit (e.g. CaCO3). Note that there are a few other non-perovskite structure types, such as aragonite, which we do not show here. We also include distorted perovskites (with tilted and rotated BO6 octahedral units) in the same category as perovskites throughout this work.« less
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    Machine learning bandgaps of double perovskites
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    • Scientific Reports, Vol. 6, Issue 1
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    journal, February 2019

    • Bartel, Christopher J.; Sutton, Christopher; Goldsmith, Bryan R.
    • Science Advances, Vol. 5, Issue 2
    • DOI: 10.1126/sciadv.aav0693

    Finding New Perovskite Halides via Machine Learning
    journal, April 2016

    • Pilania, Ghanshyam; Balachandran, Prasanna V.; Kim, Chiho
    • Frontiers in Materials, Vol. 3
    • DOI: 10.3389/fmats.2016.00019

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