Crystal-graph attention networks for the prediction of stable materials


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{
  "revision": 2, 
  "metadata": {
    "publication_date": "Dec 16, 2021, 22:28:25", 
    "_oai": {
      "id": "oai:materialscloud.org:1182"
    }, 
    "license": "Creative Commons Attribution 4.0 International", 
    "description": "Graph neural networks have enjoyed great success in the prediction of material properties for both molecules and crystals. These networks typically use the atomic positions (usually expanded in a Gaussian basis) and the atomic species as input. Unfortunately, this information is in general not available when predicting new materials, for which the precise geometrical information is unknown. In this work, we circumvent this problem by predicting the thermodynamic stability of crystal structures without using the knowledge of the precise bond distances. We replace this information with embeddings of graph distances, allowing our networks to be used directly in high-throughput studies based on both composition and crystal structure prototype. Using these embeddings, we combine the newest developments in graph neural networks and apply them to the prediction of the distances to the convex hull. To train these networks, we curate a dataset of over 2 million density-functional calculations of crystals with consistent calculation parameters from various sources. The new dataset allows for the creation of a high quality convex hull and a large scale transfer learning approach. We apply the resulting model to the high-throughput search of 15 million tetragonal perovskites of composition ABCD2. As a result, we identify several thousand potentially stable compounds and demonstrate that transfer learning from the newly curated dataset reduces the required training data by 50%.", 
    "contributors": [
      {
        "familyname": "Schmidt", 
        "affiliations": [
          "Institut f\u00fcr Physik, Martin-Luther-Universit\u00e4t Halle-Wittenberg, Halle 06120, Germany"
        ], 
        "email": "jonathan.schmidt@student.uni-halle.de", 
        "givennames": "Jonathan"
      }, 
      {
        "familyname": "Pettersson", 
        "affiliations": [
          "Department of Physics, Lund University, Box 118, 221 00 Lund, Sweden"
        ], 
        "email": "lo4642pe-s@student.lu.se", 
        "givennames": "Love"
      }, 
      {
        "familyname": "Verdozzi", 
        "affiliations": [
          "Department of Physics, Lund University, Box 118, 221 00 Lund, Sweden"
        ], 
        "email": "claudio.verdozzi@teorfys.lu.se", 
        "givennames": "Claudio"
      }, 
      {
        "familyname": "Botti", 
        "affiliations": [
          "Institut f\u00fcr Festk\u00f6rpertheorie und -optik and European Theoretical Spectroscopy Facility Friedrich-Schiller-Universit\u00e4t Jena, D-07743 Jena, Germany"
        ], 
        "email": "silvana.botti@uni-jena.de", 
        "givennames": "Silvana"
      }, 
      {
        "familyname": "Marques", 
        "affiliations": [
          "Institut f\u00fcr Physik, Martin-Luther-Universit\u00e4t Halle-Wittenberg, Halle 06120, Germany"
        ], 
        "email": "miguel.marques@physik.uni-halle.de", 
        "givennames": "Miguel"
      }
    ], 
    "edited_by": 576, 
    "title": "Crystal-graph attention networks for the prediction of stable materials", 
    "conceptrecid": "979", 
    "license_addendum": null, 
    "doi": "10.24435/materialscloud:j9-bf", 
    "mcid": "2021.222", 
    "_files": [
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        "key": "aflow_ids.tar.xz", 
        "checksum": "md5:6e48440ed4f8b17d3a7f5efd7e634870", 
        "description": "Compressed json file containing the IDs of all materials from aflow used in the work as well as space group, distance to the convex hull and formation energies."
      }, 
      {
        "size": 1835448, 
        "key": "matproj_ids.tar.xz", 
        "checksum": "md5:13e2b3859a5d4cda7179f6524d10cfb6", 
        "description": "Compressed json file containing the IDs of all materials from the materials project used in the work as well as space group, distance to the convex hull and formation energies."
      }, 
      {
        "size": 280312587, 
        "key": "computed_entries.tar.gz", 
        "checksum": "md5:1adc932b253db4a9045262d109d5a77b", 
        "description": ".tar.gz archive of computed_structure_entries.json containing the information to build computed structure entries (pymatgen object containing knowledge of the structure, energy and some calculation parameters) for all calculations from our group used in this work"
      }, 
      {
        "size": 10702107, 
        "key": "predicted_mixed_perovskites_computed_entries.tar.gz", 
        "checksum": "md5:e473354906bf6e77d478d650d50b9ab3", 
        "description": ".tar.gz archive of computed_structure_entries.json containing the information to build computed structure entries (pymatgen object containing knowledge of the structure, energy and some calculation parameters) for the predicted mixed perovskites in the paper"
      }, 
      {
        "size": 2091, 
        "key": "README.txt", 
        "checksum": "md5:93bd3c3bf87e0f9cafe9e0be01bc8c1e", 
        "description": "README"
      }
    ], 
    "id": "1182", 
    "keywords": [
      "density-functional theory", 
      "high-throughput", 
      "PBE"
    ], 
    "is_last": true, 
    "status": "published", 
    "references": [
      {
        "doi": "10.1126/sciadv.abi7948", 
        "url": "https://www.science.org/doi/abs/10.1126/sciadv.abi7948", 
        "comment": "Publication in which the data is accumulated, curated and used", 
        "type": "Journal reference", 
        "citation": "J. Schmidt, L. Pettersson, C. Verdozzi, S. Botti, M. Marques, Science Advances 7 No. 49 (2021)"
      }
    ], 
    "version": 2, 
    "owner": 491
  }, 
  "id": "1182", 
  "created": "2021-12-16T18:05:04.714160+00:00", 
  "updated": "2021-12-16T21:28:25.082776+00:00"
}