Data-science driven autonomous process optimization

An automated closed-loop system optimizes a stereoselective Suzuki-Miyaura reaction using a machine learning algorithm that incorporates unbiased and categorical process parameters.

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Detalles Bibliográficos
Autores principales:	Melodie Christensen, Lars P. E. Yunker, Folarin Adedeji, Florian Häse, Loïc M. Roch, Tobias Gensch, Gabriel dos Passos Gomes, Tara Zepel, Matthew S. Sigman, Alán Aspuru-Guzik, Jason E. Hein
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/fc4f62943ff64484b8d72e580bfeb7ba
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spelling	oai:doaj.org-article:fc4f62943ff64484b8d72e580bfeb7ba2021-12-02T16:36:40ZData-science driven autonomous process optimization10.1038/s42004-021-00550-x2399-3669https://doaj.org/article/fc4f62943ff64484b8d72e580bfeb7ba2021-08-01T00:00:00Zhttps://doi.org/10.1038/s42004-021-00550-xhttps://doaj.org/toc/2399-3669An automated closed-loop system optimizes a stereoselective Suzuki-Miyaura reaction using a machine learning algorithm that incorporates unbiased and categorical process parameters.Melodie ChristensenLars P. E. YunkerFolarin AdedejiFlorian HäseLoïc M. RochTobias GenschGabriel dos Passos GomesTara ZepelMatthew S. SigmanAlán Aspuru-GuzikJason E. HeinNature PortfolioarticleChemistryQD1-999ENCommunications Chemistry, Vol 4, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Chemistry QD1-999
spellingShingle	Chemistry QD1-999 Melodie Christensen Lars P. E. Yunker Folarin Adedeji Florian Häse Loïc M. Roch Tobias Gensch Gabriel dos Passos Gomes Tara Zepel Matthew S. Sigman Alán Aspuru-Guzik Jason E. Hein Data-science driven autonomous process optimization
description	An automated closed-loop system optimizes a stereoselective Suzuki-Miyaura reaction using a machine learning algorithm that incorporates unbiased and categorical process parameters.
format	article
author	Melodie Christensen Lars P. E. Yunker Folarin Adedeji Florian Häse Loïc M. Roch Tobias Gensch Gabriel dos Passos Gomes Tara Zepel Matthew S. Sigman Alán Aspuru-Guzik Jason E. Hein
author_facet	Melodie Christensen Lars P. E. Yunker Folarin Adedeji Florian Häse Loïc M. Roch Tobias Gensch Gabriel dos Passos Gomes Tara Zepel Matthew S. Sigman Alán Aspuru-Guzik Jason E. Hein
author_sort	Melodie Christensen
title	Data-science driven autonomous process optimization
title_short	Data-science driven autonomous process optimization
title_full	Data-science driven autonomous process optimization
title_fullStr	Data-science driven autonomous process optimization
title_full_unstemmed	Data-science driven autonomous process optimization
title_sort	data-science driven autonomous process optimization
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/fc4f62943ff64484b8d72e580bfeb7ba
work_keys_str_mv	AT melodiechristensen datasciencedrivenautonomousprocessoptimization AT larspeyunker datasciencedrivenautonomousprocessoptimization AT folarinadedeji datasciencedrivenautonomousprocessoptimization AT florianhase datasciencedrivenautonomousprocessoptimization AT loicmroch datasciencedrivenautonomousprocessoptimization AT tobiasgensch datasciencedrivenautonomousprocessoptimization AT gabrieldospassosgomes datasciencedrivenautonomousprocessoptimization AT tarazepel datasciencedrivenautonomousprocessoptimization AT matthewssigman datasciencedrivenautonomousprocessoptimization AT alanaspuruguzik datasciencedrivenautonomousprocessoptimization AT jasonehein datasciencedrivenautonomousprocessoptimization
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Data-science driven autonomous process optimization

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