Diversifying databases of metal organic frameworks for high-throughput computational screening

By combining metal nodes and organic linkers, an infinite number of metal organic frameworks (MOFs) can be designed in silico. When making new databases of such hypothetical MOFs, we need to assure that they not only contribute towards the growth of the count of structures but also add different chemistry to existing databases. In this work, we designed a database of ~20,000 hypothetical MOFs which are diverse in terms of their chemical design space—metal nodes, organic linkers, functional groups and pore geometries. Using Machine Learning techniques, we visualized and quantified the diversity of these structures. We then assessed the usefulness of diverse structures by evaluating their performance, using grand-canonical Monte Carlo simulations, in two important environmental applications---post combustion carbon capture and hydrogen storage. We find that many of these structures perform better than widely used benchmark materials such as Zeolite-13X (for post combustion carbon capture) and MOF-5 (for hydrogen storage).

通过将金属节点与有机配体相结合，可在计算机上模拟设计出无穷多种金属有机框架（metal organic frameworks, MOFs）。在构建此类假想MOFs的新型数据库时，我们需要确保这些结构不仅能扩充数据库的结构总量，还能为现有数据库引入多样化的化学属性。本研究构建了一个包含约20000种假想MOFs的数据库，这些结构在金属节点、有机配体、官能团以及孔道几何结构等化学设计空间维度上均具备丰富多样性。本研究借助机器学习（Machine Learning）技术，对这些结构的多样性进行了可视化表征与量化分析。随后，我们通过巨正则蒙特卡洛（grand-canonical Monte Carlo）模拟，评估了这些多样化结构在两类重要环境应用中的性能，以此衡量其应用价值——这两类应用分别为燃烧后碳捕获与氢气存储。研究发现，其中诸多结构在对应应用中的表现优于当前广泛使用的基准材料，例如用于燃烧后碳捕获的沸石-13X（Zeolite-13X）以及用于氢气存储的MOF-5。