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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spelling	oai:doaj.org-article:ada6eb69fea0458aa467799a13aadb442021-12-02T18:14:09ZElement selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry10.1038/s41467-021-25343-72041-1723https://doaj.org/article/ada6eb69fea0458aa467799a13aadb442021-09-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-25343-7https://doaj.org/toc/2041-1723Machine 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.Andrij VasylenkoJacinthe GamonBenjamin B. DuffVladimir V. GusevLuke M. DanielsMarco ZanellaJ. Felix ShinPaul M. SharpAlexandra MorscherRuiyong ChenAlex R. NealeLaurence J. HardwickJohn B. ClaridgeFrédéric BlancMichael W. GaultoisMatthew S. DyerMatthew J. RosseinskyNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q 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 Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry
description	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.
format	article
author	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
author_facet	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
author_sort	Andrij Vasylenko
title	Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry
title_short	Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry
title_full	Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry
title_fullStr	Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry
title_full_unstemmed	Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry
title_sort	element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ada6eb69fea0458aa467799a13aadb44
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Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry

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