Reticular frameworks are crystalline porous materials that form via the self-assembly of molecular building blocks in different topologies, with many having desirable properties for gas storage, separation, catalysis, biomedical applications and so on. The notable variety of building blocks makes reticular chemistry both promising and challenging for prospective materials design. Here we propose an automated nanoporous materials discovery platform powered by a supramolecular variational autoencoder for the generative design of reticular materials. We demonstrate the automated design process with a class of metal–organic framework (MOF) structures and the goal of separating carbon dioxide from natural gas or flue gas. Our model shows high fidelity in capturing MOF structural features. We show that the autoencoder has a promising optimization capability when jointly trained with multiple top adsorbent candidates identified for superior gas separation. MOFs discovered here are strongly competitive against some of the best-performing MOFs/zeolites ever reported.

网状骨架是晶体多孔材料，通过不同拓扑结构的分子构建块的自组装形成，其中许多具有气体存储、分离、催化、生物医学应用等所需的特性。显着的各种构建块使网状化学对于前瞻性材料设计既充满希望又具有挑战性。在这里，我们提出了一个由超分子变分自动编码器驱动的自动纳米多孔材料发现平台，用于网状材料的生成设计。我们展示了具有一类金属有机框架 (MOF) 结构的自动化设计过程，以及从天然气或烟气中分离二氧化碳的目标。我们的模型在捕捉 MOF 结构特征方面表现出高保真度。我们表明，当与多个被确定为具有出色气体分离性能的顶级吸附剂候选物联合训练时，自动编码器具有有前途的优化能力。这里发现的 MOF 与一些曾经报道过的性能最好的 MOF/沸石具有很强的竞争力。