New tolerance factor to predict the stability of perovskite oxides and halides
Top Cited Papers
Open Access
- 1 February 2019
- journal article
- research article
- Published by American Association for the Advancement of Science (AAAS) in Science Advances
- Vol. 5 (2), eaav0693
- https://doi.org/10.1126/sciadv.aav0693
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. 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.Keywords
Funding Information
- National Science Foundation (CBET-1433521)
- U.S. Department of Energy (EERE DE-EE0008088)
- Horizon 2020 (676580)
This publication has 43 references indexed in Scilit:
- Perovskites in catalysis and electrocatalysisScience, 2017
- Big Data of Materials Science: Critical Role of the DescriptorPhysical Review Letters, 2015
- The high-throughput highway to computational materials designNature Materials, 2013
- How Evolutionary Crystal Structure Prediction Works—and WhyAccounts of Chemical Research, 2011
- Crystal structure prediction from first principlesNature Materials, 2008
- Minima hopping: An efficient search method for the global minimum of the potential energy surface of complex molecular systemsThe Journal of Chemical Physics, 2004
- Thermodynamics of Global OptimizationPhysical Review Letters, 1998
- Optimization by Simulated AnnealingScience, 1983
- THE PRINCIPLES DETERMINING THE STRUCTURE OF COMPLEX IONIC CRYSTALSJournal of the American Chemical Society, 1929
- Die Gesetze der KrystallochemieThe Science of Nature, 1926