# A GPT-4 Reticular Chemist for Guiding MOF Discovery

arXiv: https://arxiv.org/abs/2306.14915 · HTML: https://mlsanity.com/paper/2306.14915/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 4 references that are in the map.
- First posted on arXiv 20 Jun 2023.

## Built on by (most cited first, 9 of 9)

- [MOFGPT: Generative Design of Metal-Organic Frameworks using Language Models](https://mlsanity.com/paper/2506.00198/) (2025)
- [The Rise of Generative AI for Metal-Organic Framework Design and Synthesis](https://mlsanity.com/paper/2508.13197/) (2025)
- [Single and Multi-Hop Question-Answering Datasets for Reticular Chemistry with GPT-4-Turbo](https://mlsanity.com/paper/2405.02128/) (2024)
- [Reshaping MOFs text mining with a dynamic multi-agents framework of large language model](https://mlsanity.com/paper/2504.18880/) (2025)
- [Leveraging large language models for nano synthesis mechanism explanation: solid foundations or mere conjectures?](https://mlsanity.com/paper/2407.08922/) (2024)
- [Unveiling the Potential of AI for Nanomaterial Morphology Prediction](https://mlsanity.com/paper/2406.02591/) (2024)
- [DropMicroFluidAgents (DMFAs): Autonomous Droplet Microfluidic Research Framework Through Large Language Model Agents](https://mlsanity.com/paper/2501.14772/) (2024)
- [Mechanisms of Matter: Language Inferential Benchmark on Physicochemical Hypothesis in Materials Synthesis](https://mlsanity.com/paper/2509.25281/) (2025)
- [Developing an AI Course for Synthetic Chemistry Students](https://mlsanity.com/paper/2511.18244/) (2025)

## Abstract (arXiv)

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.