Cargando…
Data-science driven autonomous process optimization
Autonomous process optimization involves the human intervention-free exploration of a range process parameters to improve responses such as product yield and selectivity. Utilizing off-the-shelf components, we develop a closed-loop system for carrying out parallel autonomous process optimization exp...
Autores principales: | Christensen, Melodie, Yunker, Lars P. E., Adedeji, Folarin, Häse, Florian, Roch, Loïc M., Gensch, Tobias, dos Passos Gomes, Gabriel, Zepel, Tara, Sigman, Matthew S., Aspuru-Guzik, Alán, Hein, Jason E. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9814253/ https://www.ncbi.nlm.nih.gov/pubmed/36697524 http://dx.doi.org/10.1038/s42004-021-00550-x |
Ejemplares similares
-
ChemOS: An orchestration software to democratize autonomous discovery
por: Roch, Loïc M., et al.
Publicado: (2020) -
Automation isn't automatic
por: Christensen, Melodie, et al.
Publicado: (2021) -
Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratories
por: Häse, Florian, et al.
Publicado: (2018) -
Phoenics: A Bayesian Optimizer for Chemistry
por: Häse, Florian, et al.
Publicado: (2018) -
Designing and understanding light-harvesting devices with machine learning
por: Häse, Florian, et al.
Publicado: (2020)