Algorithms for the self-optimisation of chemical reactions. Issue 9 (12th August 2019) - Clayton, Adam D.; Manson, Jamie A.; Taylor, Connor J.; Chamberlain, Thomas W.; Taylor, Brian A.; Clemens, Graeme; Bourne, Richard A.
Abstract : 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. Abstract : 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.