arXiv:2602.20210v2 Announce Type: replace Abstract: Crystal modeling spans a family of conditional and unconditional generation tasks, including crystal structure prediction (CSP) and de novo generation (DNG). While recent deep generative models have shown promising performance, they remain largely task-specific, lacking a unified framework that shares crystal representations across tasks. To address this limitation, we propose Multimodal Crystal Flow (MCFlow), a unified multimodal flow model that realizes multiple crystal generation tasks as distinct inference trajectories via independent tim
The continuous advancements in deep generative models and the increasing demand for efficient materials discovery are driving innovation in unified crystal modeling frameworks.
This development could significantly accelerate the discovery and design of novel materials with specific properties, impacting fields from semiconductors to pharmaceuticals.
Crystal modeling moves from task-specific approaches to more unified, multimodal frameworks, simplifying and broadening the scope of material generation tasks.
- · Materials Science Researchers
- · Pharmaceutical Industry
- · Semiconductor Industry
- · Chemical Engineering
- · Traditional Materials Discovery Methods
- · Ad-hoc AI-based Material Generation Startups
Accelerated design and discovery of new materials for various industrial applications.
Potential for new intellectual property and competitive advantages in material-intensive industries.
Reduced resource consumption and improved efficiency in manufacturing processes due to optimized material properties.
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Read at arXiv cs.LG