CrystalAR: Autoregressive Modeling for Symmetric Crystal Generation
To address these challenges, we propose CrystalAR, a unified autoregressive framework that models Wyckoff symmetry templates and complete crystal structures within a single generative sequence.
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Key points
- Deep generative modeling has achieved remarkable progress in crystal generation.
- Given the rich symmetries inherent in crystalline structures, recent work has sought to explicitly incorporate space-group symmetry into generative models, enabling more effective exploration of novel symmetry templates.
- Yet introducing symmetry templates increases the combinatorial complexity of stoichiometric relationships between atomic species and crystallographic sites, while creating a fundamental tension between flexible exploration of discrete crystallographic templates and faithful realization of these templates as continuous three-dimensional structures.
- These results suggest that symmetry-template generation can be retained alongside broadly competitive de novo generation performance and competitive CSP without reliance on symmetry templates retrieved from prior data, providing a promising alternative paradigm for symmetry-preserving crystal generation.
Sources (1)
- [1]CrystalAR: Autoregressive Modeling for Symmetric Crystal GenerationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 07:26 AM
To address these challenges, we propose CrystalAR, a unified autoregressive framework that models Wyckoff symmetry templates and complete crystal structures within a single generative sequence.
Deep generative modeling has achieved remarkable progress in crystal generation.
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