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Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry.
Vasylenko, Andrij; Gamon, Jacinthe; Duff, Benjamin B; Gusev, Vladimir V; Daniels, Luke M; Zanella, Marco; Shin, J Felix; Sharp, Paul M; Morscher, Alexandra; Chen, Ruiyong; Neale, Alex R; Hardwick, Laurence J; Claridge, John B; Blanc, Frédéric; Gaultois, Michael W; Dyer, Matthew S; Rosseinsky, Matthew J.
Afiliación
  • Vasylenko A; Department of Chemistry, University of Liverpool, Liverpool, UK.
  • Gamon J; Department of Chemistry, University of Liverpool, Liverpool, UK.
  • Duff BB; Department of Chemistry, University of Liverpool, Liverpool, UK.
  • Gusev VV; Stephenson Institute for Renewable Energy, University of Liverpool, Liverpool, UK.
  • Daniels LM; Department of Chemistry, University of Liverpool, Liverpool, UK.
  • Zanella M; Leverhulme Research Centre for Functional Materials Design, Materials Innovation Factory, University of Liverpool, Liverpool, UK.
  • Shin JF; Department of Chemistry, University of Liverpool, Liverpool, UK.
  • Sharp PM; Department of Chemistry, University of Liverpool, Liverpool, UK.
  • Morscher A; Department of Chemistry, University of Liverpool, Liverpool, UK.
  • Chen R; Department of Chemistry, University of Liverpool, Liverpool, UK.
  • Neale AR; Leverhulme Research Centre for Functional Materials Design, Materials Innovation Factory, University of Liverpool, Liverpool, UK.
  • Hardwick LJ; Department of Chemistry, University of Liverpool, Liverpool, UK.
  • Claridge JB; Department of Chemistry, University of Liverpool, Liverpool, UK.
  • Blanc F; Department of Chemistry, University of Liverpool, Liverpool, UK.
  • Gaultois MW; Stephenson Institute for Renewable Energy, University of Liverpool, Liverpool, UK.
  • Dyer MS; Department of Chemistry, University of Liverpool, Liverpool, UK.
  • Rosseinsky MJ; Stephenson Institute for Renewable Energy, University of Liverpool, Liverpool, UK.
Nat Commun ; 12(1): 5561, 2021 Sep 21.
Article en En | MEDLINE | ID: mdl-34548485
The selection of the elements to combine delimits the possible outcomes of synthetic chemistry because it determines the range of compositions and structures, and thus properties, that can arise. For example, in the solid state, the elemental components of a phase field will determine the likelihood of finding a new crystalline material. Researchers make these choices based on their understanding of chemical structure and bonding. Extensive data are available on those element combinations that produce synthetically isolable materials, but it is difficult to assimilate the scale of this information to guide selection from the diversity of potential new chemistries. Here, we show that unsupervised machine learning captures the complex patterns of similarity between element combinations that afford reported crystalline inorganic materials. This model guides prioritisation of quaternary phase fields containing two anions for synthetic exploration to identify lithium solid electrolytes in a collaborative workflow that leads to the discovery of Li3.3SnS3.3Cl0.7. The interstitial site occupancy combination in this defect stuffed wurtzite enables a low-barrier ion transport pathway in hexagonal close-packing.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2021 Tipo del documento: Article Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2021 Tipo del documento: Article Pais de publicación: Reino Unido