Chem4DLLM: 4D Multimodal LLMs for Chemical Dynamics Captioning

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 simple molecular motion, gas-phase reactions 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 explanations across these settings. We further develop a suite of 4D molecular language models, including 4D Text-based, 4D-MolT5 and Chem4DLLM. Notably, Chem4DLLM is a trajectory-aware model that integrates an equivariant graph encoder with a pretrained large language model to capture molecular geometry and temporal dynamics. This model suite enables a systematic evaluation of how different 4D representations and architectures support dynamic chemical understanding. We hope that ChemDU, together with Chem4DBench and our 4D model suite, will stimulate further research in dynamic chemical understanding and multimodal scientific reasoning.