Marina R. Filip^1*, Feliciano Giustino^1,2*

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

DOI10.24435/materialscloud:2018.0012/v1 [version v1]

Publication date: Sep 03, 2018

How to cite this record

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

Materials Cloud sections using this data

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

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Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

Perovskites Structure predictions Goldschmidt Data mining