CrystalDiT: Simple Diffusion Transformers for Crystal Generation

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 8.78% SUN (Stable, Unique, Novel) rate on MP-20, substantially outperforming recent methods including FlowMM (4.21%) and MatterGen (3.66%). 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.

Authors

• Xiaohan Yi Shenzhen International Graduate School, Tsinghua University Tencent, Shenzhen, China
• Guikun Xu Shanghai Jiao Tong University
• Zhong Zhang
• Liu Liu Tencent AI Lab
• Yatao Bian Tencent AI Lab
• Xi Xiao Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China Peng Cheng Laboratory, Shenzhen, China Key Laboratory of Cyberspace Security, Ministry of Education of China, Zhengzhou, China
• Peilin Zhao Tencent AI Lab

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