ChemDFM-X: Towards Large Multimodal Model for Chemistry

• URL: http://arxiv.org/abs/2409.13194v1
• Date: Fri, 20 Sep 2024 03:55:34 GMT
• Title: ChemDFM-X: Towards Large Multimodal Model for Chemistry
• Authors: Zihan Zhao, Bo Chen, Jingpiao Li, Lu Chen, Liyang Wen, Pengyu Wang, Zichen Zhu, Danyang Zhang, Ziping Wan, Yansi Li, Zhongyang Dai, Xin Chen, Kai Yu,
• Abstract summary: We introduce the first Cross-modal Dialogue Foundation Model for Chemistry (ChemDFM-X) Diverse multimodal data are generated from an initial modality by approximate calculations and task-specific model predictions. ChemDFM-X is evaluated on extensive experiments of different chemical tasks with various data modalities.
• Score: 16.811223849365483
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Rapid developments of AI tools are expected to offer unprecedented assistance to the research of natural science including chemistry. However, neither existing unimodal task-specific specialist models nor emerging general large multimodal models (LMM) can cover the wide range of chemical data modality and task categories. To address the real demands of chemists, a cross-modal Chemical General Intelligence (CGI) system, which serves as a truly practical and useful research assistant utilizing the great potential of LMMs, is in great need. In this work, we introduce the first Cross-modal Dialogue Foundation Model for Chemistry (ChemDFM-X). Diverse multimodal data are generated from an initial modality by approximate calculations and task-specific model predictions. This strategy creates sufficient chemical training corpora, while significantly reducing excessive expense, resulting in an instruction-tuning dataset containing 7.6M data. After instruction finetuning, ChemDFM-X is evaluated on extensive experiments of different chemical tasks with various data modalities. The results demonstrate the capacity of ChemDFM-X for multimodal and inter-modal knowledge comprehension. ChemDFM-X marks a significant milestone toward aligning all modalities in chemistry, a step closer to CGI.

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