The geometric blueprint of perovskites

Authors: Marina R. Filip1*, Feliciano Giustino1,2*

  1. Department of Materials, University of Oxford, Parks Road, OX3 7SP, UK
  2. Department of Materials Science and Engineering, Cornell University, Ithaca, NY 14853
  • Corresponding authors emails: marina.filip@materials.ox.ac.uk, feliciano.giustino@materials.ox.ac.uk

(version: v1, submitted on: 03 September 2018)

How to cite this entry

DOI10.24435/materialscloud:2018.0012/v1

Marina R. Filip, Feliciano Giustino, The geometric blueprint of perovskites, Materials Cloud Archive (2018), doi: 10.24435/materialscloud:2018.0012/v1.

Description

Perovskite minerals form an essential component of the Earth’s mantle, and synthetic crystals are ubiquitous in electronics, photonics, and energy technology. The extraordinary chemical diversity of these crystals raises the question of how many and which perovskites are yet to be discovered. Here we show that the “no-rattling” principle postulated by Goldschmidt in 1926, describing the geometric conditions under which a perovskite can form, is much more effective than previously thought and allows us to predict perovskites with a fidelity of 80%. By supplementing this principle with inferential statistics and internet data mining we establish that currently known perovskites are only the tip of the iceberg, and we enumerate 90,000 hitherto-unknown compounds awaiting to be studied. Our results suggest that geometric blueprints may enable the systematic screening of millions of compounds and offer untapped opportunities in structure prediction and materials design.

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Files

File name Size Description
Database_S1.1.pdf
MD5MD5: 44fa0970aa2062b91b3fd75eff8ed2c5
217.9 KiB Database of known perovskites
Database_S1.1.csv
MD5MD5: 804abe04560e513739aaa376c7586b21
94.5 KiB Database of known perovskites (CSV format)
Database_S1.2.pdf
MD5MD5: 32045b33d98ead925573a5c9efb103e3
124.1 KiB Database of known non-perovskites
Database_S1.2.csv
MD5MD5: 390b0efa08169d07250493d3fa21a180
32.1 KiB Database of known non-perovskites (CSV format)
Database_S1.3.pdf
MD5MD5: 1c9f45295ee02d5acd0d1bdd190f0c71
64.9 KiB Database of known compounds that can be both perovskites and non-perovskites
Database_S1.3.csv
MD5MD5: e1f7b72ce6858abfecc69b62f1ee34f7
4.5 KiB Database of known compounds that can be both perovskites and non-perovskites (CSV format)
Database_S2.1.csv
MD5MD5: 303b0cbcc21e88d19be0b18f7c7ae251
887.9 KiB Database of predicted compounds that have never been made or mentioned before (CSV format)
Database_S2.2.1.pdf
MD5MD5: eb632fdc3e1dce1ce33e1ac9df6de7f3
84.9 KiB Database of predicted compounds that were found on the internet and are perovskites
Database_S2.2.1.csv
MD5MD5: 9bb492cbaafa93247e9ada3699b57269
26.3 KiB Database of predicted compounds that were found on the internet and are perovskites (CSV format)
Database_S2.2.2.pdf
MD5MD5: 924805fe9b4c5868286405bfb21468f4
59.4 KiB Database of predicted compounds that were found on the internet and are not perovskites
Database_S2.2.2.csv
MD5MD5: 330852f7cdcd6419c0313fc11531ce76
10.8 KiB Database of predicted compounds that were found on the internet and are not perovskites (CSV format)

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.

Keywords

Perovskites Structure predictions Goldschmidt Data mining

Version history

03 September 2018 [This version]