We introduce GHP-MOFassemble, a Generative artificial intelligence (AI), High Performance framework to accelerate the rational design of metal-organic frameworks (MOFs) with high CO2 capacity and synthesizable linkers. Our framework combines a diffusion model, a class of generative AI, to generate novel linkers that are assembled with one of three pre-selected nodes into MOFs in a primitive cubic (pcu) topology. The CO2 capacities of these AI-generated MOFs are predicted using a modified version of the crystal graph convolutional neural network model. We then use the LAMMPS code to perform molecular dynamics simulations to relax the AI-generated MOF structures, and identify those that converge to stable structures, and maintain their porous properties throughout the simulations. Among 120,000 pcu MOF candidates generated by the GHP-MOFassemble framework, with three distinct metal nodes (Cu paddlewheel, Zn paddlewheel, Zn tetramer), a total of 102 structures completed molecular dynamics simulations at 1 bar with predicted CO2 capacity higher than 2 mmol/g at 0.1 bar, which corresponds to the top 5% of hMOFs in the hypothetical MOF (hMOF) dataset in the MOFX-DB database. Among these candidates, 18 have change in density lower than 1% during molecular dynamics simulations, indicating their stability. We also found that the top five GHP-MOFassemble's MOF structures have CO2 capacities higher than 96.9% of hMOF structures. This new approach combines generative AI, graph modeling, large-scale molecular dynamics simulations, and extreme scale computing to open up new pathways for the accelerated discovery of novel MOF structures at scale.

翻译：我们提出了GHP-MOFassemble，一个生成式人工智能（AI）高性能框架，旨在加速理性设计具有高CO₂容量和可合成连接子的金属有机框架（MOF）。该框架结合了生成式AI的一种——扩散模型，用于生成新型连接子，这些连接子与三个预选节点之一组装成具有原始立方（pcu）拓扑结构的MOF。这些AI生成MOF的CO₂容量通过改进版的晶体图卷积神经网络模型进行预测。随后，我们利用LAMMPS代码进行分子动力学模拟以弛豫AI生成的MOF结构，并识别那些收敛至稳定结构且在模拟过程中保持多孔性质的结构。在GHP-MOFassemble框架生成的120,000个pcu MOF候选结构中，涵盖三种不同金属节点（Cu桨轮、Zn桨轮、Zn四聚体），共有102个结构在0.1 bar压力下完成了分子动力学模拟，且预测CO₂容量高于2 mmol/g，这对应于MOFX-DB数据库中假设MOF（hMOF）数据集前5%的水平。在这些候选结构中，18个结构在分子动力学模拟期间密度变化低于1%，表明其稳定性。我们还发现，GHP-MOFassemble排名前五的MOF结构的CO₂容量高于96.9%的hMOF结构。这种新方法通过结合生成式AI、图建模、大规模分子动力学模拟及极限尺度计算，为规模化加速发现新型MOF结构开辟了新途径。