Combining Bayesian optimization and automation to simultaneously optimize reaction conditions and routes.
Chem Sci
; 15(20): 7732-7741, 2024 May 22.
Article
en En
| MEDLINE
| ID: mdl-38784737
ABSTRACT
Reaching optimal reaction conditions is crucial to achieve high yields, minimal by-products, and environmentally sustainable chemical reactions. With the recent rise of artificial intelligence, there has been a shift from traditional Edisonian trial-and-error optimization to data-driven and automated approaches, which offer significant advantages. Here, we showcase the capabilities of an integrated platform; we conducted simultaneous optimizations of four different terminal alkynes and two reaction routes using an automation platform combined with a Bayesian optimization platform. Remarkably, we achieved a conversion rate of over 80% for all four substrates in 23 experiments, covering ca. 0.2% of the combinatorial space. Further analysis allowed us to identify the influence of different reaction parameters on the reaction outcomes, demonstrating the potential for expedited reaction condition optimization and the prospect of more efficient chemical processes in the future.
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1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Chem Sci
Año:
2024
Tipo del documento:
Article
Pais de publicación:
Reino Unido