Algorithms for the self-optimisation of chemical reactions

被引:106
作者
Clayton, Adam D. [1 ,2 ]
Manson, Jamie A. [1 ,2 ]
Taylor, Connor J. [1 ,2 ]
Chamberlain, Thomas W. [1 ,2 ]
Taylor, Brian A. [3 ]
Clemens, Graeme [3 ]
Bourne, Richard A. [1 ,2 ]
机构
[1] Univ Leeds, Sch Chem, Inst Proc Res & Dev, Leeds LS2 9JT, W Yorkshire, England
[2] Univ Leeds, Sch Chem & Proc Engn, Leeds LS2 9JT, W Yorkshire, England
[3] AstraZeneca Pharmaceut Dev, Silk Rd Business Pk, Macclesfield SK10 2NA, Cheshire, England
基金
英国工程与自然科学研究理事会;
关键词
CONTINUOUS-FLOW; MULTIOBJECTIVE OPTIMIZATION; MULTITARGET OPTIMIZATION; MICROFLUIDIC SYSTEM; GAUSSIAN-PROCESSES; GENETIC ALGORITHM; PLATFORM; CHEMISTRY; DESIGN; INTELLIGENT;
D O I
10.1039/c9re00209j
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
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.
引用
收藏
页码:1545 / 1554
页数:10
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