用原子级生成模型突破金属有机框架设计瓶颈
Building-Block Aware Generative Modeling for 3D Crystals of Metal Organic Frameworks
- 基于三维原子表示与拓扑结构显式建模，学习构建块特征
- 可生成含1000原子的超大晶胞MOFs，几何合理且化学多样
- 预测的新型配位结构已成功合成，适合材料研发人员
金属-有机框架（MOFs）将无机节点、有机连接体与拓扑网络结合成可编程多孔晶体，但其巨大的设计空间难以通过穷举合成。生成建模具有巨大潜力，但现有模型或仅复用已知构建块，或局限于小晶胞。本文提出建筑块感知的MOF扩散模型（BBA MOF Diffusion），一种SE(3)-等变扩散模型，学习单个建筑块的3D全原子表征，并显式编码晶体学拓扑网络。在CoRE-MOF数据库上训练后，该模型能高效生成含1000原子的大晶胞MOFs，具备出色的几何合理性、新颖性与多样性，与实验数据库一致。其原生建筑块表示生成前所未见的金属节点与有机连接体，使可访问化学空间扩大数个数量级。一个高分预测的[Zn(1,4-TDC)(EtOH)2] MOF已被成功合成，粉末X射线衍射、热重分析与N2吸附均证实其结构准确性。BBA-Diff为可合成且高性能的MOFs提供了实用路径。
原文摘要 · Abstract (English)
Metal-organic frameworks (MOFs) marry inorganic nodes, organic edges, and topological nets into programmable porous crystals, yet their astronomical design space defies brute-force synthesis. Generative modeling holds ultimate promise, but existing models either recycle known building blocks or are restricted to small unit cells. We introduce Building-Block-Aware MOF Diffusion (BBA MOF Diffusion), an SE(3)-equivariant diffusion model that learns 3D all-atom representations of individual building blocks, encoding crystallographic topological nets explicitly. Trained on the CoRE-MOF database, BBA MOF Diffusion readily samples MOFs with unit cells containing 1000 atoms with great geometric validity, novelty, and diversity mirroring experimental databases. Its native building-block representation produces unprecedented metal nodes and organic edges, expanding accessible chemical space by orders of magnitude. One high-scoring [Zn(1,4-TDC)(EtOH)2] MOF predicted by the model was synthesized, where powder X-ray diffraction, thermogravimetric analysis, and N2 sorption confirm its structural fidelity. BBA-Diff thus furnishes a practical pathway to synthesizable and high-performing MOFs.
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