baixuefeng/ChemReasoner-7B

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.

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🚀 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.

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📊 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.

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📥 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.

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⚙️ 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

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📈 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.

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📜 License

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

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📚 Citation

If you use MOFReasoner in your research, please cite:

@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},
}

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📬 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)