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 apprentice. 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 apprentice provides feedback
on the experimental outcomes, including both success and failures, for the
in-text 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 GPT-4
Reticular Chemist demonstrated the discovery of an isoreticular series of
metal-organic frameworks (MOFs), each of which was made using distinct
synthesis strategies and optimal conditions. 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.

本研究基于人 - AI 交互合作，整合了 AI 模型 GPT-4 到网状化学实验的迭代过程中，通过自然语言开发和操作实现。使用 GPT-4 提供的详细指导进行实验，与学徒提供实验结果反馈进行文本学习，从而实现像有经验的化学家那样的快速学习策略。该工作流程在合成多种优化条件的金属有机骨架时表现出潜在的广泛应用前景。