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.
Guardado en:
Autores principales: | , , , , , , , , , , |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/fc4f62943ff64484b8d72e580bfeb7ba |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
id |
oai:doaj.org-article:fc4f62943ff64484b8d72e580bfeb7ba |
---|---|
record_format |
dspace |
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 |
_version_ |
1718383670821126144 |