  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.
