The way to AI-controlled synthesis: how far do we need to go?

被引:8
作者
Wang, Wei [1 ]
Liu, Yingwei [2 ]
Wang, Zheng [1 ]
Hao, Gefei [1 ]
Song, Baoan [1 ]
机构
[1] Guizhou Univ, Res & Dev Ctr Fine Chem, State Key Lab Breeding Base Green Pesticide & Agr, Key Lab Green Pesticide & Agr Bioengn,Minist Educ, Guiyang 550025, Peoples R China
[2] Guizhou Univ, State Key Lab Publ Big Data, Guiyang 550025, Peoples R China
基金
中国国家自然科学基金;
关键词
NEURAL-NETWORK MODEL; DESIGN; DIGITIZATION; PREDICTION; PLATFORM; SYSTEM; NMR; IR;
D O I
10.1039/d2sc04419f
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Chemical synthesis always plays an irreplaceable role in chemical, materials, and pharmacological fields. Meanwhile, artificial intelligence (AI) is causing a rapid technological revolution in many fields by replacing manual chemical synthesis and has exhibited a much more economical and time-efficient manner. However, the rate-determining step of AI-controlled synthesis systems is rarely mentioned, which makes it difficult to apply them in general laboratories. Here, the history of developing AI-aided synthesis has been overviewed and summarized. We propose that the hardware of AI-controlled synthesis systems should be more adaptive to execute reactions with different phase reagents and under different reaction conditions, and the software of AI-controlled synthesis systems should have richer kinds of reaction prediction modules. An updated system will better address more different kinds of syntheses. Our viewpoint could help scientists advance the revolution that combines AI and synthesis to achieve more progress in complicated systems.
引用
收藏
页码:12604 / 12615
页数:12
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