Atomized chemical knowledge, such as functional group information of molecules and reactions, plays a pivotal intermediate role in the reasoning process that connects molecular structures with their properties and reactivities. While large language models (LLMs) have achieved impressive progress, the absence of atomized chemical knowledge results in their superficial understanding of chemistry and limited chemical reasoning capabilities. In this work, to tackle this problem, we develop a Chemical Reasoning LLM, ChemDFM-R. We first construct a comprehensive dataset of atomized chemical knowledge, ChemFG, annotating the presence of functional groups in molecules and the changes of functional groups during chemical reactions, to enhance the model's understanding of the fundamental principles and internal logic of chemistry. Then, we propose a mixed-source distillation method that initializes the model's reasoning capability with limited distilled data, and develop a four-stage training pipeline to equip the model with atomized chemical knowledge and chemical reasoning logic. Experiments on diverse chemical benchmarks demonstrate that ChemDFM-R achieves cutting-edge performance while providing interpretable, rationale-driven outputs, surpassing both the general-domain LLMs and domain-specific chemical LLMs. Moreover, ChemDFM-R achieves comparable or superior performance compared with cutting-edge commercial LLMs, such as o4-mini. Further case studies illustrate how explicit reasoning chains significantly improve the model's reliability, transparency, and practicality in real-world human-AI collaboration scenarios.

翻译：原子化化学知识（如分子与反应中的官能团信息）在连接分子结构与其性质及反应活性的推理过程中扮演着关键的中介角色。尽管大语言模型（LLMs）已取得显著进展，但原子化化学知识的缺失导致其对化学本质理解浅薄，化学推理能力受限。为解决这一问题，本文开发了化学推理大语言模型ChemDFM-R。首先构建了原子化化学知识综合数据集ChemFG，标注了分子中的官能团存在性及化学反应中的官能团变化，以增强模型对化学基本原理与内在逻辑的理解。继而提出混合源蒸馏方法，利用有限蒸馏数据初始化模型推理能力，并设计四阶段训练流程使模型掌握原子化化学知识与化学推理逻辑。在多种化学基准测试中的实验表明，ChemDFM-R在提供可解释且基于推理链的输出同时，实现了前沿性能，超越了通用领域大语言模型和化学领域专用大语言模型。此外，ChemDFM-R在性能上与o4-mini等前沿商用大语言模型相当或更优。进一步的案例分析展示了显式推理链如何显著提升模型在真实人机协作场景中的可靠性、透明性与实用性。