Chem4DLLM: 4D Multimodal LLMs for Chemical Dynamics Understanding

• URL: http://arxiv.org/abs/2603.11924v1
• Date: Thu, 12 Mar 2026 13:36:58 GMT
• Title: Chem4DLLM: 4D Multimodal LLMs for Chemical Dynamics Understanding
• Abstract summary: ChemDU translates 4D molecular trajectories into interpretable natural-language explanations.<n>ChemDU focuses on fundamental dynamic scenarios, including gas-phase and catalytic reactions.<n>Chem4DBench is the first dataset pairing 4D molecular trajectories with expert-authored explanations.
• Score: 52.499532583297885
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• MolSight: A Graph-Aware Vision-Language Model for Unified Chemical Image Understanding [25.768840369692608]
  MolSight is a graph-aware vision-language model framework designed to enhance the understanding of molecular images.<n>Our experiments demonstrate that MolSight significantly outperforms existing VLMs, molecular LLMs, and specialized tools across multiple chemical visual understanding tasks.
  arXiv  Detail & Related papers  (2026-07-02T10:13:19Z)
• Agentic reinforcement learning empowers next-generation chemical language models for molecular design and synthesis [51.83339196548892]
  ChemCraft is a novel framework that decouples chemical reasoning from knowledge storage.<n>ChemCraft achieves superior performance with minimal inference costs.<n>This work establishes a cost-effective and privacy-preserving paradigm for AI-aided chemistry.
  arXiv  Detail & Related papers  (2026-01-25T04:23:34Z)
• Functional Groups are All you Need for Chemically Interpretable Molecular Property Prediction [0.0]
  This work proposes developing molecule representations using the concept of Functional Groups (FG) in chemistry.<n>We introduce the Functional Group Representation (FGR) framework, a novel approach to encoding molecules based on their fundamental chemical substructures.<n>We demonstrate that the FGR framework achieves state-of-the-art performance on a diverse range of 33 benchmark datasets.
  arXiv  Detail & Related papers  (2025-09-11T17:01:31Z)
• $\ ext{M}^{2}$LLM: Multi-view Molecular Representation Learning with Large Language Models [59.125833618091846]
  We propose a multi-view framework that integrates three perspectives: the molecular structure view, the molecular task view, and the molecular rules view.<n>Experiments demonstrate that $textM2$LLM achieves state-of-the-art performance on multiple benchmarks across classification and regression tasks.
  arXiv  Detail & Related papers  (2025-08-12T05:46:47Z)
• Constructing and explaining machine learning models for chemistry: example of the exploration and design of boron-based Lewis acids [0.0]
  We leverage explainable AI techniques to explore the rational design of boron-based Lewis acids.<n>Lewis acids play a pivotal role in organic reactions due to their electron-ccepting properties.<n>By constraining the chemical space to well-defined molecular scaffolds, we achieved highly accurate predictions.
  arXiv  Detail & Related papers  (2025-01-02T23:47:54Z)
• ChemVLM: Exploring the Power of Multimodal Large Language Models in Chemistry Area [70.66610054938052]
  We introduce textbfChemVLM, an open-source chemical multimodal large language model for chemical applications.<n>ChemVLM is trained on a carefully curated bilingual dataset that enhances its ability to understand both textual and visual chemical information.<n>We benchmark ChemVLM against a range of open-source and proprietary multimodal large language models on various tasks.
  arXiv  Detail & Related papers  (2024-08-14T01:16:40Z)
• ChemLLM: A Chemical Large Language Model [49.308528569982805]
  Large language models (LLMs) have made impressive progress in chemistry applications. However, the community lacks an LLM specifically designed for chemistry. Here, we introduce ChemLLM, a comprehensive framework that features the first LLM dedicated to chemistry.
  arXiv  Detail & Related papers  (2024-02-10T01:11:59Z)
• Domain-Agnostic Molecular Generation with Chemical Feedback [44.063584808910896]
  MolGen is a pre-trained molecular language model tailored specifically for molecule generation. It internalizes structural and grammatical insights through the reconstruction of over 100 million molecular SELFIES. Our chemical feedback paradigm steers the model away from molecular hallucinations, ensuring alignment between the model's estimated probabilities and real-world chemical preferences.
  arXiv  Detail & Related papers  (2023-01-26T17:52:56Z)
• Improving Molecular Pretraining with Complementary Featurizations [20.86159731100242]
  Molecular pretraining is a paradigm to solve a variety of tasks in computational chemistry and drug discovery. We show that different featurization techniques convey chemical information differently. We propose a simple and effective MOlecular pretraining framework with COmplementary featurizations (MOCO)
  arXiv  Detail & Related papers  (2022-09-29T21:11:09Z)
• Learning Latent Space Energy-Based Prior Model for Molecule Generation [59.875533935578375]
  We learn latent space energy-based prior model with SMILES representation for molecule modeling. Our method is able to generate molecules with validity and uniqueness competitive with state-of-the-art models.
  arXiv  Detail & Related papers  (2020-10-19T09:34:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.