让模型看懂分子动态变化并用自然语言解释。
Chem4DLLM: 4D Multimodal LLMs for Chemical Dynamics Understanding
- 用4D轨迹+语言模型理解化学反应动态过程。
- 构建首个配对分子轨迹与专家解释的数据集。
- 适合关注动态化学与多模态科学推理的研究者。
现有化学理解任务主要依赖静态分子表示，难以建模键断裂、构象变化等固有动态现象，而这正是化学家理解反应的关键。为此，我们提出化学动态理解（ChemDU）新任务，将4D分子轨迹转化为可解释的自然语言说明。ChemDU聚焦气相和催化反应等基本动态场景，要求模型识别轨迹中的关键事件，如键的形成与解离，并生成机制合理、连贯的叙述。为评估该能力，我们构建了首个数据集Chem4DBench，涵盖多种场景下4D分子轨迹与专家撰写的解释。我们进一步提出Chem4DLLM，一个融合等变图编码器与预训练大语言模型的统一架构，显式捕捉分子几何与旋转动力学。我们期望ChemDU、Chem4DBench与Chem4DLLM能推动动态化学理解与多模态科学推理研究。
原文摘要 · Abstract (English)
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.
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