From the start of a synthetic chemist’s training,
experiments
are conducted based on recipes from textbooks and manuscripts that
achieve clean reaction outcomes, allowing the scientist to develop
practical skills and some chemical intuition. This procedure is often
kept long into a researcher’s career, as new recipes are developed
based on similar reaction protocols, and intuition-guided deviations
are conducted through learning from failed experiments. However, when
attempting to understand chemical systems of interest, it has been
shown that model-based, algorithm-based, and miniaturized high-throughput
techniques outperform human chemical intuition and achieve reaction
optimization in a much more time- and material-efficient manner; this
is covered in detail in this paper. As many synthetic chemists are
not exposed to these techniques in undergraduate teaching, this leads
to a disproportionate number of scientists that wish to optimize their
reactions but are unable to use these methodologies or are simply
unaware of their existence. This review highlights the basics, and
the cutting-edge, of modern chemical reaction optimization as well
as its relation to process scale-up and can thereby serve as a reference
for inspired scientists for each of these techniques, detailing several
of their respective applications.
Self-optimising chemical systems have experienced a growing momentum in recent years. Herein, we review algorithms used for the self-optimisation of chemical reactions in an accessible way for the general chemist.
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