New tolerance factor to predict the stability of perovskite oxides and halides
By Christopher J. Bartel, Christopher Sutton, Bryan R. Goldsmith, Runhai Ouyang, C. Musgrave, Luca M. Ghiringhelli, Matthias Scheffler. Science Advances, 2018
1,477 citations recorded by the source corpus.
Simple and interpretable data-driven descriptor accurately predicts the synthesizability of single and double perovskites. 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.
Subfields: Electrical and Electronic Engineering, Electronic, Optical and Magnetic Materials, Materials Chemistry
View Paper: https://www.semanticscholar.org/paper/85fb1a2d89def00db5f4f87115bff80b0828ba47
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In Electrical and Electronic Engineering
In Materials Chemistry
- Rohit Batra
- A. Gencer
- A. Candan
- Rampi Ramprasad
- H. H. Gullu
- Mehmet Isik
- J. Esterhuizen
- Christopher Sutton
Citations in the corpus
- Machine learning for heterogeneous catalyst design and discovery (2018, methodology): “SISSO has been used by some of the authors to find an improved descriptor to predict the stability of perovskite oxide and halide materials using an experimental dataset.(63) The linearity and simplicity of the descriptors found by SISSO can make them more transferable to materials outside of the tr”