ChemDFM-X: Towards Large Multimodal Model for Chemistry
This addresses the need for a practical research assistant for chemists by aligning diverse chemical data modalities, though it is incremental as it builds on existing large multimodal models.
The authors tackled the lack of a comprehensive AI assistant for chemistry by introducing ChemDFM-X, a cross-modal dialogue foundation model, which was trained on 7.6M data points and demonstrated capacity for multimodal knowledge comprehension in chemical tasks.
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.