Year: 2026
Type: preprint
Abstract: Making a new reaction often requires chemical knowledge that does not yet exist. Self-driving laboratories and language-model agents now plan, run and optimize experiments rapidly, but deciding what to ask next still requires a chemist in the loop Here we report AIChemist, a robotic chemist for what we call constructive chemical discovery. It decides what to ask next from its own results, settles each question by calculation or experiment, and builds the next design on the answer. Given a silyl-radical-relay concept, AIChemist developed an asymmetric three-component silylcyanation: it established that the solvent effect depended on the oxidant formulation and built the next formulation on this finding, raising the yield from 20% to 67% at 88% enantiomeric excess. For an intermediate of the insecticide broflanilide, it designed a photoredox/nickel C–N coupling route free of nitro-group transformations, at lower estimated cost than the conventional route. When the base favoured by calculation performed poorly, it withdrew that premise and rebuilt the search around the reaction medium, where non-polar toluene raised the product's HPLC yield from 43% to 86%. In both cases, each conclusion extended what the system knew and became the starting point for the next. These results demonstrate a framework for autonomous chemical discovery in which chemical understanding and experimental design evolve together within a closed loop.
Source: ChemRxiv
Authors: Yuzhi Richie Xu, Max Wang, Zilong Yan, Yu Jiang, Yong Lu, Yuting Wang, Haohui Que, Zhengming Zhang, Letian Chen, Zhenjia Gan, Haotian Wu, Yilin Wei, Chengyi Xing, Wenxi Zhai, Bowen Li, Daqian Bian, Yuli Li, Chengwei Ju, Jiankai Ge, Yingcheng Wu, Yuanqing Wang, Pheng Ann Heng, Dong Xing, Tong Zhu
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7223677679