Finetuning-Free Diffusion Model with Adaptive Constraint Guidance for Inorganic Crystal Structure Generation
arXiv:2604.13354v2 Announce Type: replace-cross Abstract: The discovery of inorganic crystal structures with targeted properties is a significant challenge in materials science. Generative models, especially state-of-the-art diffusion models, offer the promise of modeling complex data distributions and proposing novel, realistic samples. However, current generative AI models still struggle to produce diverse, original, and reliable structures of experimentally achievable materials suitable for high-stakes applications. In this work, we propose a generative machine learning framework based on d
The rapid advancements in generative AI, particularly diffusion models, are pushing their application into complex scientific domains like materials science.
This development indicates a potential acceleration in the discovery and design of novel inorganic materials with targeted properties, impacting various industries.
The ability to generate diverse, original, and reliable crystal structures without extensive finetuning could significantly reduce the time and cost associated with materials research and development.
- · Materials Science Researchers
- · Chemical Industry
- · Semiconductor Manufacturers
- · Energy Technology Developers
- · Traditional Materials Research Methods
- · Labs with limited AI integration
Accelerated discovery of new inorganic crystal structures for high-stakes applications.
New materials lead to breakthroughs in energy storage, computing, and sustainable technologies.
The development of a 'design-first' approach to materials engineering, potentially outstripping empirical discovery.
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Read at arXiv cs.AI