Machine learning-accelerated discovery of A₂BC₂ ternary electrides with diverse anionic electron densities


JSON Export

{
  "id": "1956", 
  "updated": "2023-11-28T12:27:31.186120+00:00", 
  "metadata": {
    "version": 1, 
    "contributors": [
      {
        "givennames": "Zhiqi", 
        "affiliations": [
          "State Key Laboratory of Solidification Processing, School of Materials Science and Engineering, Northwestern Polytechnical University, Xi\u2019an, Shaanxi 710072, People\u2019s Republic of China."
        ], 
        "familyname": "Wang"
      }, 
      {
        "givennames": "Yutong", 
        "affiliations": [
          "State Key Laboratory of Solidification Processing, School of Materials Science and Engineering, Northwestern Polytechnical University, Xi\u2019an, Shaanxi 710072, People\u2019s Republic of China."
        ], 
        "familyname": "Gong"
      }, 
      {
        "givennames": "Matthew L.", 
        "affiliations": [
          "IMCN-MODL, Universit\u00e9 catholique de Louvain, Chemin des \u00c9toiles, 8, B-1348 Louvain-la-Neuve, Belgium."
        ], 
        "email": "matthew.evans@uclouvain.be", 
        "familyname": "Evans"
      }, 
      {
        "givennames": "Yujing", 
        "affiliations": [
          "State Key Laboratory of Solidification Processing, School of Materials Science and Engineering, Northwestern Polytechnical University, Xi\u2019an, Shaanxi 710072, People\u2019s Republic of China."
        ], 
        "familyname": "Yan"
      }, 
      {
        "givennames": "Shiyao", 
        "affiliations": [
          "State Key Laboratory of Solidification Processing, School of Materials Science and Engineering, Northwestern Polytechnical University, Xi\u2019an, Shaanxi 710072, People\u2019s Republic of China."
        ], 
        "familyname": "Wang"
      }, 
      {
        "givennames": "Nanxi", 
        "affiliations": [
          "State Key Laboratory of Solidification Processing, School of Materials Science and Engineering, Northwestern Polytechnical University, Xi\u2019an, Shaanxi 710072, People\u2019s Republic of China."
        ], 
        "familyname": "Miao"
      }, 
      {
        "givennames": "Ruiheng", 
        "affiliations": [
          "State Key Laboratory of Solidification Processing, School of Materials Science and Engineering, Northwestern Polytechnical University, Xi\u2019an, Shaanxi 710072, People\u2019s Republic of China."
        ], 
        "familyname": "Zheng"
      }, 
      {
        "givennames": "Gian-Marco", 
        "affiliations": [
          "State Key Laboratory of Solidification Processing, School of Materials Science and Engineering, Northwestern Polytechnical University, Xi\u2019an, Shaanxi 710072, People\u2019s Republic of China.", 
          "IMCN-MODL, Universit\u00e9 catholique de Louvain, Chemin des \u00c9toiles, 8, B-1348 Louvain-la-Neuve, Belgium."
        ], 
        "email": "gian-marco.rignanese@uclouvain.be", 
        "familyname": "Rignanese"
      }, 
      {
        "givennames": "Junjie", 
        "affiliations": [
          "State Key Laboratory of Solidification Processing, School of Materials Science and Engineering, Northwestern Polytechnical University, Xi\u2019an, Shaanxi 710072, People\u2019s Republic of China."
        ], 
        "email": "wang.junjie@nwpu.edu.cn", 
        "familyname": "Wang"
      }
    ], 
    "title": "Machine learning-accelerated discovery of A\u2082BC\u2082 ternary electrides with diverse anionic electron densities", 
    "_oai": {
      "id": "oai:materialscloud.org:1956"
    }, 
    "keywords": [
      "density-functional theory", 
      "machine learning", 
      "electrides"
    ], 
    "publication_date": "Nov 28, 2023, 13:27:31", 
    "_files": [
      {
        "key": "mp_comparison.json.gz", 
        "description": "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).", 
        "checksum": "md5:0fd258a43490647f7a8a8e1c6270cce5", 
        "size": 50204
      }, 
      {
        "key": "data.csv", 
        "description": "Computed ELF max and stability info for each structure", 
        "checksum": "md5:f55f59f16bd2be446d7c54e433f725e5", 
        "size": 18955
      }, 
      {
        "key": "raw.tar.gz", 
        "description": "Additional raw data, including bandstructures and phonon dispersion curves for every structure.", 
        "checksum": "md5:ccc0c64b527d8a6ba5eefbeb4c912e57", 
        "size": 344092261
      }, 
      {
        "key": "structures.tar.gz", 
        "description": "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).", 
        "checksum": "md5:cc20ae389a4d608767a1f2efd30023a9", 
        "size": 41396
      }, 
      {
        "key": "scripts.zip", 
        "description": "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.", 
        "checksum": "md5:a06ee4e54e27b5d2ec81cc67faf2a97c", 
        "size": 2992
      }, 
      {
        "key": "README.txt", 
        "description": "README", 
        "checksum": "md5:8ebb190b9b43d6f574b4c75541ff15be", 
        "size": 1039
      }, 
      {
        "key": "optimade.yaml", 
        "description": "A config file for the MCloud/OPTIMADE integration that allows ingestion of the data into an OPTIMADE API", 
        "checksum": "md5:8aae8284e20f59d83d6b00e4cb691ea3", 
        "size": 1558
      }
    ], 
    "references": [
      {
        "comment": "", 
        "doi": "10.1021/jacs.3c10538", 
        "citation": "Z. Wang et al., J. Amer. Chem. Soc. (2023)", 
        "url": "https://doi.org/10.1021/jacs.3c10538", 
        "type": "Journal reference"
      }
    ], 
    "description": "This study combines machine learning (ML) and high-throughput calculations to uncover new ternary electrides in the A\u2082BC\u2082 family of compounds with the P4/mbm space group. Starting from a library of 214 known A\u2082BC\u2082 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.", 
    "status": "published", 
    "license": "Creative Commons Attribution 4.0 International", 
    "conceptrecid": "1955", 
    "is_last": true, 
    "mcid": "2023.181", 
    "edited_by": 576, 
    "id": "1956", 
    "owner": 1179, 
    "license_addendum": null, 
    "doi": "10.24435/materialscloud:c8-gy"
  }, 
  "revision": 7, 
  "created": "2023-10-30T18:42:10.521139+00:00"
}