arXiv:2511.14426v2 Announce Type: replace Abstract: In recent years, diffusion-based models have demonstrated exceptional performance in searching for simultaneously stable, unique, and novel (S.U.N.) crystalline materials. However, most of these models don't have the ability to change the number of atoms in the crystal during the generation process, which limits the variability of model sampling trajectories. In this paper, we demonstrate the severity of this restriction and introduce a simple yet powerful technique, mirage infusion, which enables diffusion models to change the state of the a
The continuous advancements in AI, particularly diffusion models, are pushing the boundaries of generative design in materials science, making this innovation a natural progression.
This development could significantly accelerate the discovery and design of novel materials with specific properties, impacting multiple industries reliant on advanced materials.
Diffusion models can now dynamically adjust the number of atoms during crystal generation, enabling the creation of a wider and more diverse range of stable, unique, and novel (S.U.N.) crystalline materials.
- · Materials science researchers
- · Chemical companies
- · Semiconductor manufacturers
- · AI algorithm developers
- · Traditional empirical materials discovery methods
Faster and cheaper discovery of new materials for various applications.
Reduced dependence on trial-and-error experimentation in materials research, leading to more efficient R&D cycles.
The development of entirely new classes of materials with previously unattainable properties, revolutionizing industries from energy to electronics.
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