Bayesian reaction optimization as a tool for chemical synthesis

Reaction optimization is fundamental to synthetic chemistry, from optimizing the yield of industrial processes to selecting conditions for the preparation of medicinal candidates 1 . Likewise, parameter optimization is omnipresent in artificial intelligence, from tuning virtual personal assistants t...

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Vydáno v:	Nature (London) Ročník 590; číslo 7844; s. 89 - 96
Hlavní autoři:	Shields, Benjamin J., Stevens, Jason, Li, Jun, Parasram, Marvin, Damani, Farhan, Alvarado, Jesus I. Martinez, Janey, Jacob M., Adams, Ryan P., Doyle, Abigail G.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 04.02.2021 Nature Publishing Group
Témata:	119/118 140/131 639/638/549/973 639/638/905 639/705/1046 639/705/117 Algorithms Artificial intelligence Automation Bayes Theorem Bayesian analysis Chemical synthesis Chemistry Chemistry Techniques, Synthetic - methods Chemists Computer & video games Datasets as Topic Decision Making Design of experiments Digital media Expected utility Global optimization Halogenation Humanities and Social Sciences Iterative methods Laboratories Learning algorithms Machine learning multidisciplinary Open source software Optimization algorithms Palladium Palladium - chemistry Parameters Public domain Reproducibility of Results Response surface methodology Science Science (multidisciplinary) Software Software development tools Source code Tuning
ISSN:	0028-0836, 1476-4687, 1476-4687
On-line přístup:	Získat plný text
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