Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry

Autor: Andrij Vasylenko, Jacinthe Gamon, Benjamin B. Duff, Vladimir V. Gusev, Luke M. Daniels, Marco Zanella, J. Felix Shin, Paul M. Sharp, Alexandra Morscher, Ruiyong Chen, Alex R. Neale, Laurence J. Hardwick, John B. Claridge, Frédéric Blanc, Michael W. Gaultois, Matthew S. Dyer, Matthew J. Rosseinsky
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Nature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
Druh dokumentu: article
ISSN: 2041-1723
DOI: 10.1038/s41467-021-25343-7
Popis: Machine learning has the potential to significantly speed-up the discovery of new materials in synthetic materials chemistry. Here the authors combine unsupervised machine learning and crystal structure prediction to predict a novel quaternary lithium solid electrolyte that is then synthesized.
Databáze: Directory of Open Access Journals
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