Journal
REACTION CHEMISTRY & ENGINEERING
Volume 4, Issue 9, Pages 1545-1554Publisher
ROYAL SOC CHEMISTRY
DOI: 10.1039/c9re00209j
Keywords
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Funding
- EPSRC
- University of Leeds
- AstraZeneca
- EPSRC project Cognitive Chemical Manufacturing [EP/R032807/1]
- EPSRC [EP/R032807/1, 1803783] Funding Source: UKRI
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Self-optimising chemical systems have experienced a growing momentum in recent years, with the evolution of self-optimising platforms leading to their application for reaction screening and chemical synthesis. With the desire for improved process sustainability, self-optimisation provides a cheaper, faster and greener approach to the chemical development process. The use of such platforms aims to enhance the capabilities of the researcher by removing the need for labor-intensive experimentation, allowing them to focus on more challenging tasks. The establishment of these systems have enabled opportunities for self-optimising platforms to become a key element of a laboratory's repertoire. To enable the wider adoption of self-optimising chemical platforms, this review summarises the history of algorithmic usage in chemical reaction self-optimisation, detailing the functionality of the algorithms and their applications in a way that is accessible for chemists and highlights opportunities for the further exploitation of algorithms in chemical synthesis moving forward.
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