A GPT-4 Reticular Chemist for Guiding MOF Discovery

TL;DR

This paper introduces a human-AI collaborative framework using GPT-4 to guide MOF discovery through iterative, natural language-based experimentation and feedback, making advanced research accessible to chemists without coding skills.

Contribution

It presents a novel iterative workflow integrating GPT-4 into reticular chemistry, enabling non-coders to collaboratively discover MOFs via natural language interactions.

Findings

Successful discovery of an isoreticular series of MOFs.

GPT-4 learned from experimental outcomes to improve guidance.

The system is accessible to chemists without programming knowledge.

Abstract

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.