化学家
网状结缔组织
药物发现
化学
医学
生物化学
解剖
有机化学
作者
Zhiling Zheng,Zichao Rong,Nakul Rampal,Christian Borgs,Jennifer Chayes,Omar M. Yaghi
出处
期刊:Angewandte Chemie
[Wiley]
日期:2023-10-05
卷期号:62 (46): e202311983-e202311983
被引量:137
标识
DOI:10.1002/anie.202311983
摘要
We present a new framework integrating the AI model GPT-4 into the iterative process of reticular chemistry experimentation, leveraging a cooperative workflow of interaction between AI and a human researcher. This GPT-4 Reticular Chemist is an integrated system composed of three phases. Each of these utilizes GPT-4 in various capacities, wherein GPT-4 provides detailed instructions for chemical experimentation and the human provides feedback on the experimental outcomes, including both success and failures, for the in-context learning of AI in the next iteration. This iterative human-AI interaction enabled GPT-4 to learn from the outcomes, much like an experienced chemist, by a prompt-learning strategy. Importantly, the system is based on natural language for both development and operation, eliminating the need for coding skills, and thus, make it accessible to all chemists. Our collaboration with GPT-4 Reticular Chemist guided the discovery of an isoreticular series of MOFs, with each synthesis fine-tuned through iterative feedback and expert suggestions. This workflow presents a potential for broader applications in scientific research by harnessing the capability of large language models like GPT-4 to enhance the feasibility and efficiency of research activities.
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