High-quality data enabling universality of band-gap descriptor and discovery of photovoltaic perovskites

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{
  "metadata": {
    "edited_by": 576, 
    "owner": 699, 
    "_oai": {
      "id": "oai:materialscloud.org:2149"
    }, 
    "description": "Extensive machine-learning assisted research has been dedicated to predicting band gaps for perovskites, driven by their immense potential in photovoltaics. Yet, the effectiveness is often hampered by the lack of high-quality band-gap datasets, particularly for perovskites involving d orbitals. In this work, we consistently calculate a large dataset of band gaps with a high level of accuracy, which is rigorously validated by experimental and state-of-the-art GW band gaps. Leveraging this achievement, our machine-learning derived descriptor exhibits exceptional universality and robustness, proving effectiveness not only for single and double, halide and oxide perovskites regardless of the underlying atomic structures, but also for hybrid organic-inorganic perovskites. With this approach, we comprehensively explore up to 15,659 materials, unveiling 14 unreported lead-free perovskites with suitable band gaps for photovoltaics. Notably, MASnBr\u2083, FA\u2082SnGeBr\u2086, MA\u2082AuAuBr\u2086, FA\u2082AuAuBr\u2086, FA\u2082InBiCl\u2086, FA\u2082InBiBr\u2086, and Ba\u2082InBiO\u2086 stand out with direct band gaps, small effective masses, low exciton binding energies, and high stabilities.", 
    "mcid": "2024.61", 
    "id": "2149", 
    "license": "Creative Commons Attribution 4.0 International", 
    "license_addendum": null, 
    "references": [
      {
        "citation": "H. Y. Wang, R. H. Ouyang, W. Chen, and A. Pasquarello, J. Am. Chem. Soc., 2024.", 
        "type": "Journal reference"
      }
    ], 
    "doi": "10.24435/materialscloud:ma-ge", 
    "keywords": [
      "Photoboltaic perovskites", 
      "Band-gap descriptor", 
      "machine learning", 
      "Hybrid functional", 
      "High quality dataset"
    ], 
    "contributors": [
      {
        "affiliations": [
          "Chaire de Simulation \u00e0 l\u2019Echelle Atomique (CSEA), Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland."
        ], 
        "familyname": "Wang", 
        "email": "haiyuan.wang@epfl.ch", 
        "givennames": "Haiyuan"
      }, 
      {
        "affiliations": [
          "Materials Genome Institute, Shanghai University, Shanghai, 200444, China"
        ], 
        "familyname": "Ouyang", 
        "email": "rouyang@shu.edu.cn", 
        "givennames": "Runhai"
      }, 
      {
        "affiliations": [
          "Institute of Condensed Matter and Nanosciences, Universit\u00e9 catholique de Louvain (UCLouvain), B-1348 Louvain-la-Neuve, Belgium."
        ], 
        "familyname": "Chen", 
        "givennames": "Wei"
      }, 
      {
        "affiliations": [
          "Chaire de Simulation \u00e0 l\u2019Echelle Atomique (CSEA), Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland."
        ], 
        "familyname": "Pasquarello", 
        "givennames": "Alfredo"
      }
    ], 
    "conceptrecid": "2148", 
    "version": 1, 
    "publication_date": "Apr 23, 2024, 09:56:03", 
    "is_last": true, 
    "status": "published", 
    "_files": [
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        "size": 13503373, 
        "checksum": "md5:2e2d59a318922206cc1949217bcedca9", 
        "description": "The semicore-based pseudopotentials have been purposely generated for this present study, and used for DSH band-gap calculations. The PBE pseudopotentials are generated using exactly the same parameters as for the PseudoDojo ONCVPSP-v0.4 except that nonlinear core correction is disabled. When multiple pseudopotentials are available for a single element, the most stringent one (i.e., with semicore states and a \"high\" suffix in the filename) are used in the present study.", 
        "key": "0.4_no_nlcc_DSH.zip"
      }, 
      {
        "size": 12028141, 
        "checksum": "md5:0f006d6ae2d07158cdff02a50f19344c", 
        "description": "The semicore-based pseudopotentials have been purposely generated for this present study, and used for DSH+SOC band-gap calculations. The PBE pseudopotentials are generated using exactly the same parameters as for the PseudoDojo ONCVPSP-v0.4 except that nonlinear core correction is disabled. When multiple pseudopotentials are available for a single element, the most stringent one (i.e., with semicore states and a \"high\" suffix in the filename) are used in the present study.", 
        "key": "0.4_fr_no_nlcc_DSH+SOC.zip"
      }, 
      {
        "size": 213146, 
        "checksum": "md5:8762114a7663aafefac49a0d679c51d4", 
        "description": "The 246 structures used in this work to construct the DSH band-gap dataset, with all provided structures fully relaxed using the PBE functional.", 
        "key": "246_structures.zip"
      }, 
      {
        "size": 13469, 
        "checksum": "md5:6d03dca39a36f2bced17cd20c5b6746b", 
        "description": "The calculated band gaps with the functional of PBE and DSH including the spin-orbit coupling effects, as well as the corresponding dielectric infinity for the 246 materials in our dataset. The calculated band gaps determined using the PBE and DSH functionals, incorporating spin-orbit coupling effects, as well as the corresponding dielectric infinity values, for the 246 materials in our dataset", 
        "key": "datasets_246_DSHgap+dielectric_constant.xlsx"
      }, 
      {
        "size": 42059, 
        "checksum": "md5:e9e26203ecabd6285f4cc23c9466da56", 
        "description": "The band gaps obtained using DSH+SOC vs. using the machine learning model [i.e. equation 1 (3D@1C) in the main text], as well as the adopted input features. These results are illustrated in figures 3a and 3b in the main text.", 
        "key": "ML_gap_figure3a&b.xlsx"
      }, 
      {
        "size": 41496, 
        "checksum": "md5:670abcfb233e656c6e56a08dc77a5f8d", 
        "description": "The band gaps obtained using DSH+SOC vs. using the machine learning model [equation S1 3D@4C(dielectric_infinity)] in the Supporting Information, as well as the adopted input features. These results are illustrated in figures S5a and S5b in the Supporting Information.", 
        "key": "ML_gap_figureS5a&b.xlsx"
      }, 
      {
        "size": 80769, 
        "checksum": "md5:1e8a8515c9609155cecf898d0f264bab", 
        "description": "The 3D@1C machine learning model applied to Materials Project (Database I), including various information such as the screening criteria, predicted band gaps, DSH+SOC band gaps, and their respective input features.", 
        "key": "Database_I_screening.xlsx"
      }, 
      {
        "size": 212474, 
        "checksum": "md5:ffc507aea1c697575cf5565506e1e115", 
        "description": "The 3D@1C machine learning model applied to Database I, including various information such as the screening criteria, predicted band gaps, DSH+SOC band gaps, and their respective input features.", 
        "key": "Database_II_screening.xlsx"
      }
    ], 
    "title": "High-quality data enabling universality of band-gap descriptor and discovery of photovoltaic perovskites"
  }, 
  "id": "2149", 
  "updated": "2024-04-23T07:56:03.272494+00:00", 
  "created": "2024-04-21T12:49:19.325031+00:00", 
  "revision": 6
}