Existing chemical understanding tasks primarily rely on static molecular representations, limiting their ability to model inherently dynamic phenomena such as bond breaking or conformational changes, which are essential for a chemist to understand chemical reactions. To address this gap, we introduce Chemical Dynamics Understanding (ChemDU), a new task that translates 4D molecular trajectories into interpretable natural-language explanations. ChemDU focuses on fundamental dynamic scenarios, including gas-phase and catalytic reactions, and requires models to reason about key events along molecular trajectories, such as bond formation and dissociation, and to generate coherent, mechanistically grounded narratives. To benchmark this capability, we construct Chem4DBench, the first dataset pairing 4D molecular trajectories with expert-authored explanations across these settings. We further propose Chem4DLLM, a unified model that integrates an equivariant graph encoder with a pretrained large language model to explicitly capture molecular geometry and rotational dynamics. We hope that ChemDU, together with Chem4DBench and Chem4DLLM, will stimulate further research in dynamic chemical understanding and multimodal scientific reasoning.

翻译：现有的化学理解任务主要依赖于静态分子表征，这限制了其模拟本质上动态现象（如键断裂或构象变化）的能力，而这些现象对于化学家理解化学反应至关重要。为填补这一空白，我们引入了化学动力学理解（ChemDU）这一新任务，其目标是将四维分子轨迹转化为可解释的自然语言描述。ChemDU聚焦于基础动态场景，包括气相反应与催化反应，要求模型能够对分子轨迹中的关键事件（如键的形成与解离）进行推理，并生成连贯且基于机理的叙述。为评估此能力，我们构建了Chem4DBench，这是首个在上述场景中将四维分子轨迹与专家撰写的解释配对的基准数据集。我们进一步提出了Chem4DLLM，这是一个统一模型，它将等变图编码器与预训练大语言模型相结合，以显式捕获分子几何结构与旋转动力学。我们希望ChemDU，连同Chem4DBench和Chem4DLLM，能够推动动态化学理解与多模态科学推理领域的进一步研究。