×

Recommended by

Indexed by

Finding new crystalline compounds using chemical similarity

Hai-Chen Wang1, Silvana Botti2*, Miguel A. L. Marques1*

1 Institut für Physik, Martin-Luther-Universität Halle-Wittenberg, 06120 Halle (Saale), Germany.

2 Institut für Festkörpertheorie und -optik and European Theoretical Spectroscopy Facility, Friedrich-Schiller-Universität Jena, D-07743 Jena, Germany

* Corresponding authors emails: silvana.botti@uni-jena.de, miguel.marques@physik.uni-halle.de
DOI10.24435/materialscloud:96-09 [version v1]

Publication date: May 03, 2021

How to cite this record

Hai-Chen Wang, Silvana Botti, Miguel A. L. Marques, Finding new crystalline compounds using chemical similarity, Materials Cloud Archive 2021.68 (2021), doi: 10.24435/materialscloud:96-09.

Description

We proposed an efficient high-throughput scheme for the discovery of new stable crystalline phases. Our approach was based on the transmutation of known compounds, through the substitution of atoms in the crystal structure with chemically similar ones. The concept of similarity is defined quantitatively using a measure of chemical replaceability, extracted by data mining experimental databases. In this way we build more than 250k possible crystal phases, with almost 20k that are on the convex hull of stability. This dataset contains the optimized structure and the energy of these 250k materials calculated with the PBE approximation, in a format that is convenient for data-mining or for machine-learning applications.

Materials Cloud sections using this data

No Explore or Discover sections associated with this archive record.

Files

File name Size Description
README.txt
MD5md5:31bd94fd2b1c30ef3cfabe24a4cf1fa0
1.7 KiB Description of the dataset
step_1.json.bz2
MD5md5:7ed24b95cc0b80305580de69611d6819
18.0 MiB Calculations of step 1
step_2.json.bz2
MD5md5:e7d22945b5477bec226090cb936038dd
15.8 MiB Calculations of step 2
step_3.json.bz2
MD5md5:cccc16630b7836a4d88a4f3cb38f651f
24.7 MiB Calculations of step 3
step_4.json.bz2
MD5md5:083e08a8e610513b7a092b4734c7dff5
11.9 MiB Calculations of step 4
step_5.json.bz2
MD5md5:d5e5717de87d5954b3d4578a6e5aaf73
5.7 MiB Calculations of step 5
summary.txt.bz2
MD5md5:6b6ddfe2dce541758d2bcf0820fba926
2.7 MiB Summary of the data

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Journal reference (Paper where the method and the data are described.)
H.-C. Wang, S. Botti, and M.A.L. Marques, NPJ Comput. Mater. 7, 12 (2021) doi:10.1038/s41524-020-00481-6

Keywords

density-functional theory high-throughput machine learning

Version history:

2021.68 (version v1) [This version] May 03, 2021 DOI10.24435/materialscloud:96-09