CrystalDiT: A Diffusion Transformer for Crystal Generation

• URL: http://arxiv.org/abs/2508.16614v2
• Date: Wed, 27 Aug 2025 03:45:12 GMT
• Title: CrystalDiT: A Diffusion Transformer for Crystal Generation
• Authors: Xiaohan Yi, Guikun Xu, Xi Xiao, Zhong Zhang, Liu Liu, Yatao Bian, Peilin Zhao,
• Abstract summary: We present CrystalDiT, a diffusion transformer for crystal structure generation that achieves state-of-the-art performance.<n>CrystalDiT employs a unified transformer that imposes a powerful inductive bias: treating lattice and atomic properties as a single, interdependent system.
• Score: 52.45780803467369
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present CrystalDiT, a diffusion transformer for crystal structure generation that achieves state-of-the-art performance by challenging the trend of architectural complexity. Instead of intricate, multi-stream designs, CrystalDiT employs a unified transformer that imposes a powerful inductive bias: treating lattice and atomic properties as a single, interdependent system. Combined with a periodic table-based atomic representation and a balanced training strategy, our approach achieves 9.62% SUN (Stable, Unique, Novel) rate on MP-20, substantially outperforming recent methods including FlowMM (4.38%) and MatterGen (3.42%). Notably, CrystalDiT generates 63.28% unique and novel structures while maintaining comparable stability rates, demonstrating that architectural simplicity can be more effective than complexity for materials discovery. Our results suggest that in data-limited scientific domains, carefully designed simple architectures outperform sophisticated alternatives that are prone to overfitting.

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