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

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

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.

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Autores principales: 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
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
Science
Q
Acceso en línea:https://doaj.org/article/ada6eb69fea0458aa467799a13aadb44
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https://doaj.org/article/ada6eb69fea0458aa467799a13aadb44

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