Matthew Evans^1*

1 IMCN, UCLouvain

* Corresponding authors emails: matthew.evans@uclouvain.be

DOI10.24435/materialscloud:eq-8f [version v1]

Publication date: Oct 10, 2023

How to cite this record

Matthew Evans, Machine learning-accelerated discovery of A2BC2 ternary electrides with diverse anionic electron densities, Materials Cloud Archive 2023.9 (2023), https://doi.org/10.24435/materialscloud:eq-8f

Description

This study combines machine learning (ML) and high-throughput calculations to uncover new ternary electrides in the A2BC2 family of compounds with the P4/mbm space group. Starting from a library of 214 known A2BC2 phases, density-functional theory calculations were used to compute the maximum value of the electron localization function, indicating that 42 are potential electrides. A model was then trained on this dataset and used to predict the electride behaviour of 14,437 hypothetical compounds generated by structural prototyping. Then, the stability and electride features of the 1254 electride candidates predicted by the model were carefully checked by high-throughput calculations.

Materials Cloud sections using this data

Files

File name	Size	Description
README.txt MD5md5:81ae6703c57b134554f9c76a9f6a7e90	266 Bytes	README
data.csv MD5md5:24c8aa48b7bca9c28d27ef7d7f5a79c5	5.0 KiB	Please see the attached README.
optimade.yaml MD5md5:7c4bd9ebe96a687274bfab555f227bfe Go to the OPTIMADE API	1008 Bytes	OPTIMADE integration config file
raw.tar.gz MD5md5:ccc0c64b527d8a6ba5eefbeb4c912e57	328.2 MiB	Raw data before processing.
structures.tar.gz MD5md5:566a89ac004f37149e6431a88500cbf0	19.6 KiB	Archive of structural 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

Keywords

electrides DFT machine learning