ChemDFM-R: An Chemical Reasoner LLM Enhanced with Atomized Chemical
  Knowledge
Authors: name: Zihan Zhao, name: Bo Chen, name: Ziping Wan, name: Lu Chen, name: Xuanze Lin, name: Shiyang Yu, name: Situo Zhang, name: Da Ma, name: Zichen Zhu, name: Danyang Zhang, name: Huayang Wang, name: Zhongyang Dai, name: Liyang Wen, name: Xin Chen, name: Kai Yu

While large language models (LLMs) have achieved impressive progress, their
application in scientific domains such as chemistry remains hindered by shallow
domain understanding and limited reasoning capabilities. In this work, we focus
on the specific field of chemistry and develop a Chemical Reasoner LLM,
ChemDFM-R. We first construct a comprehensive dataset of atomized knowledge
points to enhance the model's understanding of the fundamental principles and
logical structure of chemistry. Then, we propose a mix-sourced distillation
strategy that integrates expert-curated knowledge with general-domain reasoning
skills, followed by domain-specific reinforcement learning to enhance chemical
reasoning. Experiments on diverse chemical benchmarks demonstrate that
ChemDFM-R achieves state-of-the-art performance while providing interpretable,
rationale-driven outputs. Further case studies illustrate how explicit
reasoning chains significantly improve the reliability, transparency, and
practical utility of the model in real-world human-AI collaboration scenarios.