Bayesian Optimization for Chemical Reactions
نویسندگان
چکیده
Reaction optimization is challenging and traditionally delegated to domain experts who iteratively propose increasingly optimal experiments. Problematically, the reaction landscape complex often requires hundreds of experiments reach convergence, representing an enormous resource sink. Bayesian (BO) algorithm that recommends next experiment based on previous observations has recently gained considerable interest in general chemistry community. The application BO for chemical reactions been demonstrated increase efficiency campaigns can recommend favorable conditions amidst many possibilities. Moreover, its ability jointly optimize desired objectives such as yield stereoselectivity makes it attractive alternative or at least complementary expert-guided optimization. With democratization software, barrier entry applying drastically lowered. intersection between paradigms will see advancements ever-rapid pace. In this review, we discuss how be transformed into machine-readable formats which learned by machine learning (ML) models. We present a foundation already applied outcomes. important message convey realizing full potential ML-augmented require close collaboration experimentalists computational scientists.
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ژورنال
عنوان ژورنال: Chimia
سال: 2023
ISSN: ['0009-4293', '2673-2424']
DOI: https://doi.org/10.2533/chimia.2023.31