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Title: New tolerance factor to predict the stability of perovskite oxides and halides

Abstract

Predicting the stability of the perovskite structure remains a long-standing challenge for the discovery of new functional materials for many applications including photovoltaics and electrocatalysts. We developed an accurate, physically interpretable, and one-dimensional tolerance factor, τ, that correctly predicts 92% of compounds as perovskite or nonperovskite for an experimental dataset of 576 ABX3 materials (X = O2–, F, Cl, Br, I) using a novel data analytics approach based on SISSO (sure independence screening and sparsifying operator). τ is shown to generalize outside the training set for 1034 experimentally realized single and double perovskites (91% accuracy) and is applied to identify 23,314 new double perovskites (A2BB'X6) ranked by their probability of being stable as perovskite. Furthermore, this work guides experimentalists and theorists toward which perovskites are most likely to be successfully synthesized and demonstrates an approach to descriptor identification that can be extended to arbitrary applications beyond perovskite stability predictions.

Authors:
ORCiD logo [1];  [2]; ORCiD logo [3];  [2]; ORCiD logo [4]; ORCiD logo [2];  [2]
  1. Univ. of Colorado, Boulder, CO (United States)
  2. Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin (Germany)
  3. Univ. of Michigan, Ann Arbor, MI (United States)
  4. Univ. of Colorado, Boulder, CO (United States); National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Transportation Office. Fuel Cell Technologies Office
OSTI Identifier:
1496850
Report Number(s):
NREL/JA-5K00-73346
Journal ID: ISSN 2375-2548
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Accepted Manuscript
Journal Name:
Science Advances
Additional Journal Information:
Journal Volume: 5; Journal Issue: 2; Journal ID: ISSN 2375-2548
Publisher:
AAAS
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; perovskites; functional materials; photovoltaics; electrocatalysts

Citation Formats

Bartel, Christopher J., Sutton, Christopher, Goldsmith, Bryan R., Ouyang, Runhai, Musgrave, Charles B., Ghiringhelli, Luca M., and Scheffler, Matthias. New tolerance factor to predict the stability of perovskite oxides and halides. United States: N. p., 2019. Web. https://doi.org/10.1126/sciadv.aav0693.
Bartel, Christopher J., Sutton, Christopher, Goldsmith, Bryan R., Ouyang, Runhai, Musgrave, Charles B., Ghiringhelli, Luca M., & Scheffler, Matthias. New tolerance factor to predict the stability of perovskite oxides and halides. United States. https://doi.org/10.1126/sciadv.aav0693
Bartel, Christopher J., Sutton, Christopher, Goldsmith, Bryan R., Ouyang, Runhai, Musgrave, Charles B., Ghiringhelli, Luca M., and Scheffler, Matthias. Fri . "New tolerance factor to predict the stability of perovskite oxides and halides". United States. https://doi.org/10.1126/sciadv.aav0693. https://www.osti.gov/servlets/purl/1496850.
@article{osti_1496850,
title = {New tolerance factor to predict the stability of perovskite oxides and halides},
author = {Bartel, Christopher J. and Sutton, Christopher and Goldsmith, Bryan R. and Ouyang, Runhai and Musgrave, Charles B. and Ghiringhelli, Luca M. and Scheffler, Matthias},
abstractNote = {Predicting the stability of the perovskite structure remains a long-standing challenge for the discovery of new functional materials for many applications including photovoltaics and electrocatalysts. We developed an accurate, physically interpretable, and one-dimensional tolerance factor, τ, that correctly predicts 92% of compounds as perovskite or nonperovskite for an experimental dataset of 576 ABX3 materials (X = O2–, F–, Cl–, Br–, I–) using a novel data analytics approach based on SISSO (sure independence screening and sparsifying operator). τ is shown to generalize outside the training set for 1034 experimentally realized single and double perovskites (91% accuracy) and is applied to identify 23,314 new double perovskites (A2BB'X6) ranked by their probability of being stable as perovskite. Furthermore, this work guides experimentalists and theorists toward which perovskites are most likely to be successfully synthesized and demonstrates an approach to descriptor identification that can be extended to arbitrary applications beyond perovskite stability predictions.},
doi = {10.1126/sciadv.aav0693},
journal = {Science Advances},
number = 2,
volume = 5,
place = {United States},
year = {2019},
month = {2}
}

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Cited by: 49 works
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Figures / Tables:

Fig. 1 Fig. 1: Perovskite structure and composition. (A) ABX3, in the cubic single perovskite structure (Pm$\overline{3}$m), where the A cation is surrounded by a network of cornersharing BX6 octahedra. (B) A2BB′X6, in the rock salt double perovskite structure (Fm $\overline{3}$m), where the A cations are surrounded by an alternating network ofmore » BX6 and B′X6 octahedra. In this structure, inverting the B and B′ cations results in an equivalent structure. While the ideal cubic structures are shown here, perovskites may also adopt various noncubic structures. (C) Map of the elements that occupy the A, B, and/or X sites within the 576 compounds experimentally characterized as perovskite or nonperovskite at ambient conditions and reported in (17–19).« less

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