MCFlow: Unified Multimodal Crystal Generation Model
Researchers propose Multimodal Crystal Flow (MCFlow), a unified deep generative model for crystal modeling tasks including crystal structure prediction and de novo generation. MCFlow uses a multimodal flow approach with independent time variables for atom types and structures, enabling multiple generation tasks within a single framework. It introduces composition- and symmetry-aware atom ordering with hierarchical permutation augmentation to incorporate crystallographic priors without explicit templates. Experiments on MP-20 and MPTS-52 benchmarks show competitive performance. The work addresses the lack of unified representation in task-specific crystal generation models.
Key facts
- MCFlow is a unified multimodal flow model for crystal generation.
- It handles crystal structure prediction and de novo generation.
- Uses independent time variables for atom types and structures.
- Introduces composition- and symmetry-aware atom ordering.
- Employs hierarchical permutation augmentation.
- Tested on MP-20 and MPTS-52 benchmarks.
- Aims to share crystal representations across tasks.
- Published on arXiv (2602.20210).
Entities
Institutions
- arXiv