Zhiqi Wang¹, Yutong Gong¹, Matthew L. Evans^2*, Yujing Yan¹, Shiyao Wang¹, Nanxi Miao¹, Ruiheng Zheng¹, Gian-Marco Rignanese^1,2*, Junjie Wang^1*

1 State Key Laboratory of Solidification Processing, School of Materials Science and Engineering, Northwestern Polytechnical University, Xi’an, Shaanxi 710072, People’s Republic of China.

2 IMCN-MODL, Université catholique de Louvain, Chemin des Étoiles, 8, B-1348 Louvain-la-Neuve, Belgium.

* Corresponding authors emails: matthew.evans@uclouvain.be, gian-marco.rignanese@uclouvain.be, wang.junjie@nwpu.edu.cn

DOI10.24435/materialscloud:c8-gy [version v1]

Publication date: Nov 28, 2023

How to cite this record

Zhiqi Wang, Yutong Gong, Matthew L. Evans, Yujing Yan, Shiyao Wang, Nanxi Miao, Ruiheng Zheng, Gian-Marco Rignanese, Junjie Wang, Machine learning-accelerated discovery of A₂BC₂ ternary electrides with diverse anionic electron densities, Materials Cloud Archive 2023.181 (2023), https://doi.org/10.24435/materialscloud:c8-gy

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
mp_comparison.json.gz MD5md5:0fd258a43490647f7a8a8e1c6270cce5	49.0 KiB	A a list of pymatgen `ComputedEntry` containing the results of atomate2 `MPGGARelax` calculations to enable direct comparison with the Materials Project's convex hull (as of 20/11/2023).
data.csv MD5md5:f55f59f16bd2be446d7c54e433f725e5	18.5 KiB	Computed ELF max and stability info for each structure
raw.tar.gz MD5md5:ccc0c64b527d8a6ba5eefbeb4c912e57	328.2 MiB	Additional raw data, including bandstructures and phonon dispersion curves for every structure.
structures.tar.gz MD5md5:cc20ae389a4d608767a1f2efd30023a9	40.4 KiB	CIFs of every structure considered, with the exact structures used for initial ELFCar calculations, as well as by the MP compatibility relaxation (in the corresponding sub-folders).
scripts.zip MD5md5:a06ee4e54e27b5d2ec81cc67faf2a97c	2.9 KiB	A directory containing Python scripts used for the re-relaxation and stability calculations of the structures, alongside a requirements file with the dependencies required for repeating them, as well as the script required to create this archive from the raw data.
README.txt MD5md5:8ebb190b9b43d6f574b4c75541ff15be	1.0 KiB	README
optimade.yaml MD5md5:8aae8284e20f59d83d6b00e4cb691ea3 Go to the OPTIMADE API	1.5 KiB	A config file for the MCloud/OPTIMADE integration that allows ingestion of the data into an OPTIMADE API

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

density-functional theory machine learning electrides