There is currently a push towards big data and data mining in materials research to accelerate discovery. Zeolites, metal–organic frameworks and other related crystalline porous materials are not immune to this phenomenon, as evidenced by the proliferation of porous structure databases and computational gas-adsorption screening studies over the past decade. The endeavour to identify the best materials for various gas separation and storage applications has led not only to thousands of synthesized structures, but also to the development of algorithms for building hypothetical materials. The materials databases assembled with these algorithms contain a much wider range of complex pore structures than have been synthesized, with the reasoning being that we have discovered only a small fraction of realizable structures and expanding upon these will accelerate rational design. In this Review, we highlight the methods developed to build these databases, and some of the important outcomes from large-scale computational screening studies.

中文翻译：

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纳米多孔材料基因组的计算开发

当前，材料研究领域正在朝着大数据和数据挖掘的方向发展，以加快发现速度。沸石，金属-有机骨架和其他相关的晶体多孔材料不能幸免于这种现象，过去十年来，多孔结构数据库的激增和计算性气体吸附筛选研究证明了这一点。为各种气体分离和存储应用确定最佳材料的努力不仅导致了数千种合成结构，而且还导致了用于构建假设材料的算法的开发。用这些算法组装的材料数据库包含的复杂孔结构范围比合成的要广泛得多，理由是，我们仅发现了可实现结构的一小部分，对其进行扩展将加快合理设计的速度。在这篇评论中，我们重点介绍了建立这些数据库的方法，以及大规模计算机筛选研究的一些重要成果。