arXiv:2605.14759v2 Announce Type: replace Abstract: De novo crystal generation seeks to discover materials that are not merely realistic, but also stable and novel. However, most existing generative models are trained to maximize the likelihood of observed crystals, which encourages samples to stay close to known materials yet not necessarily align with the criteria that matter in discovery. Our empirical analysis shows that current crystal generative models exhibit a clear conflict between stability and novelty: samples near the observed distribution tend to retain stability but offer limited
The proliferation of advanced AI techniques, particularly in generative models and self-supervised learning like JEPA, is enabling new breakthroughs in materials science.
Accelerated crystal discovery has direct implications for a wide range of industries requiring novel materials with specific properties, from electronics to energy.
This advancement changes the paradigm of materials discovery from largely empirical and iterative to one augmented by AI-driven predictive and generative capabilities, significantly shortening R&D cycles.
- · Materials science research institutions
- · Chemical and pharmaceutical industries
- · Semiconductor manufacturers
- · Renewable energy companies
- · Traditional materials discovery labs relying solely on manual methods
- · Companies slow to adopt AI in R&D processes
Faster development and deployment of materials with enhanced or novel functionalities.
Increased competition and innovation in sectors reliant on advanced materials, potentially leading to new product categories.
Reduced resource consumption due to the discovery of more efficient or sustainable materials, impacting geopolitical resource dependencies.
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