New Tolerance Factor to Predict the Stability of Perovskite Oxides and Halides: Article No. eaav0693

Charles Musgrave, Christopher Bartel, Christopher Sutton, Bryan Goldsmith, Runhai Ouyang, Luca Ghiringhelli, Matthias Scheffler

Research output: Contribution to journalArticlepeer-review

820 Scopus Citations

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, t, 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). t 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. 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.
Original languageAmerican English
Number of pages9
JournalScience Advances
Volume5
Issue number2
DOIs
StatePublished - 2019

NREL Publication Number

  • NREL/JA-5K00-73346

Keywords

  • electrocatalysts
  • functional materials
  • perovskites
  • photovoltaics

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