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baixuefeng/
ChemReasoner-7B

Name: ChemReasoner-7B
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MOFReasoner

MOFReasoner (currently released as ChemReasoner) is a domain-specific reasoning large language model (LLM) designed to think like a scientist. It integrates Chain-of-Thought (CoT) reasoning and knowledge distillation to enhance scientific reasoning in chemistry, with a particular focus on Metal-Organic Frameworks (MOFs) adsorption research.

🚀 Introduction

General-purpose large language models (LLMs) have shown impressive capabilities in natural language understanding and reasoning. However, their lack of domain specialization limits their ability to perform multi-step scientific reasoning.
MOFReasoner addresses this limitation by incorporating domain-specific knowledge, scientific reasoning strategies, and structured CoT reasoning.

Key innovations:

Domain Knowledge Integration: Leveraging over 8,200 research articles and 500 review papers to construct a domain-specific CoT dataset.
Knowledge Distillation: Transferring expertise from large teacher models (e.g., DeepSeek-V3, Qwen-Turbo, DeepSeek-R1) into smaller, efficient student models.
Scientific Reasoning Skills: Mimicking scientists’ problem-solving pathways, such as hypothesis generation, validation, and logical deduction.
Benchmarking & Applications: Evaluated on tasks including experimental studies, chemical mechanisms, application scenarios, and industrialization challenges in MOFs research.

📊 Features

Multi-step reasoning for scientific tasks (experiment design, reaction prediction, performance analysis).
Domain specialization in MOF adsorption, catalysis, and chemical mechanism exploration.
High performance compared to general-purpose LLMs (outperforming GPT-4.5, DeepSeek-R1, etc.).
Material recommendation ability with accuracy comparable to Density Functional Theory (DFT).
Adaptability: Easily extendable to other chemistry-related domains by incorporating domain CoT data.

📥 Model Access

Model weights (Hugging Face): ChemReasoner-7B
Code repository (GitHub): ChemReasoner-Code

⚠️ Note: The project will soon be renamed to MOFReasoner, but the current release is under the name ChemReasoner.

⚙️ How to Use

You can run the model directly from Hugging Face using vLLM or SGLang.

Example with vLLM

vllm serve baixuefeng/ChemReasoner-7B --tensor-parallel-size 2 --max-model-len 32768 --enforce-eager

Example with SGLang

python3 -m sglang.launch_server --model baixuefeng/ChemReasoner-7B --trust-remote-code --tp 2

📈 Performance

MOFReasoner was evaluated against leading models (DeepSeek, Qwen, GPT series, etc.) across four major task categories:

Experimental Studies of MOFs
Chemical Mechanisms of adsorption
Application Scenarios of MOF-based adsorbents
Industrialization Challenges

Highlights:

Achieved the highest expert-evaluated score (25.5/30), outperforming GPT-4.5, o1-preview, and DeepSeek-R1.
Provided more accurate and reliable reasoning chains, avoiding serious errors common in general-purpose models.
Demonstrated robust material recommendation, consistent with DFT validation.

📜 License

MOFReasoner is released under the MIT License.
Distilled base models (Qwen, LLaMA) retain their original licenses (Apache 2.0 / LLaMA license).

📚 Citation

If you use MOFReasoner in your research, please cite:

bibtex

@article{bai2025mofreasoner,
  title={MOFReasoner: Think Like a Scientist—A Domain-Specific Reasoning LLM via Knowledge Distillation},
  author={Bai, Xuefeng and Zheng, Zhiling and Wang, Hao-Tian and Yang, Rui and Zhang, Xin and Li, Jian-Rong},
}

📬 Contact

Corresponding Authors:
- Prof. Jian-Rong Li, Beijing University of Technology (jrli@bjut.edu.cn)
- Prof. Xin Zhang, Beijing University of Technology (zhang.xin@bjut.edu.cn)
Project Maintainers:
- Xuefeng Bai (Beijing University of Technology)
- Zhiling Zheng (MIT)