用分子图生成有机晶体结构，效率比现有方法高10倍以上
OrgFlow: Generative Modeling of Organic Crystal Structures from Molecular Graphs
- 基于流匹配模型，结合分子连通性与周期边界条件
- 生成匹配率超基线10倍，采样步骤更少
- 适合药物、功能材料领域的晶体结构预测
晶体结构预测是材料科学中的长期挑战，现有数据驱动方法多针对无机体系，对有机晶体覆盖不足。有机晶体在药物、聚合物和功能材料中至关重要，但具有更大的晶胞和严格的化学连接性。本文提出一种从分子图直接生成有机晶体结构的流匹配模型。该架构融合分子连通性与周期边界条件，同时保持晶体系统的对称性。通过键感知损失函数，强制约束键长和连接性分布，引导模型生成合理的局部化学结构。为支持高效训练，构建了经人工校准的有机晶体数据集，并开发预处理流程，预先计算键和边，显著降低训练与推理时的计算开销。实验表明，本方法生成匹配率超过现有基线10倍，且推理所需采样步数更少。结果确立了生成建模在有机晶体结构预测中的实用性与可扩展性。
原文摘要 · Abstract (English)
Crystal structure prediction is a long-standing challenge in materials science, with most data-driven methods developed for inorganic systems. This leaves an important gap for organic crystals, which are central to pharmaceuticals, polymers, and functional materials, but present unique challenges, such as larger unit cells and strict chemical connectivity. We introduce a flow-matching model for predicting organic crystal structures directly from molecular graphs. The architecture integrates molecular connectivity with periodic boundary conditions while preserving the symmetries of crystalline systems. A bond-aware loss guides the model toward realistic local chemistry by enforcing distributions of bond lengths and connectivity. To support reliable and efficient training, we built a curated dataset of organic crystals, along with a preprocessing pipeline that precomputes bonds and edges, substantially reducing computational overhead during both training and inference. Experiments show that our method achieves a Match Rate more than 10 times higher than existing baselines while requiring fewer sampling steps for inference. These results establish generative modeling as a practical and scalable framework for organic crystal structure prediction.
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