用流匹配方法高效生成含数百原子的金属有机框架结构。
MOFFlow: Flow Matching for Structure Prediction of Metal-Organic Frameworks
- 将金属节点和有机连接体视为刚性单元，在SE(3)空间建模其旋转平移行为。
- 可生成含数百原子的MOF结构，精度显著优于传统方法和现有机器学习模型。
- 适合材料设计、催化剂开发等需要快速生成复杂晶体结构的研究者。
金属-有机框架（MOFs）是一类具有广泛应用前景的晶态材料，如碳捕获和药物递送。本文提出MOFFlow，首个专为MOF结构预测设计的深度生成模型。现有方法（包括从头计算和深度生成模型）因晶胞中原子数量庞大而难以应对复杂结构。为此，我们提出一种新型黎曼流匹配框架，通过将金属节点和有机连接体视为刚性体，利用MOFs的固有模块化特性降低问题维度。在SE(3)空间中操作，该方法可高效捕捉刚性组件的旋转平移动态，具备良好可扩展性。实验表明，MOFFlow能准确预测包含数百原子的MOF结构，性能显著优于传统方法与当前最优机器学习基线，且速度更快。
原文摘要 · Abstract (English)
Metal-organic frameworks (MOFs) are a class of crystalline materials with promising applications in many areas such as carbon capture and drug delivery. In this work, we introduce MOFFlow, the first deep generative model tailored for MOF structure prediction. Existing approaches, including ab initio calculations and even deep generative models, struggle with the complexity of MOF structures due to the large number of atoms in the unit cells. To address this limitation, we propose a novel Riemannian flow matching framework that reduces the dimensionality of the problem by treating the metal nodes and organic linkers as rigid bodies, capitalizing on the inherent modularity of MOFs. By operating in the $SE(3)$ space, MOFFlow effectively captures the roto-translational dynamics of these rigid components in a scalable way. Our experiment demonstrates that MOFFlow accurately predicts MOF structures containing several hundred atoms, significantly outperforming conventional methods and state-of-the-art machine learning baselines while being much faster.
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