Metal–organic frameworks (MOFs) are one of the most researched designer materials today, as their high tunability offers scientists a wide space to imagine all kinds of possible structures. Their uniquely flexible customisability spurred the creation of hypothetical datasets and the syntheses of more than 100 000 MOFs officially reported in the Cambridge Structural Database. To scan such large numbers of MOFs, computational high-throughput screenings (HTS) have become the customary method to identify the most promising structure for a given application, and/or to spot useful structure–property relationships. However, despite all these data-mining efforts, only a fraction of HTS studies have identified synthesisable top-performing MOFs that were then further investigated in the lab. In this perspective, we review these specific cases and suggest possible steps to push future HTS more systematically towards synthesisable structures.

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从计算高通量筛选到实验室：从计算机中取出金属有机框架

金属有机框架 (MOF) 是当今研究最多的设计材料之一，因为它们的高可调性为科学家提供了广阔的空间来想象各种可能的结构。它们独特的灵活可定制性促进了假设数据集的创建和剑桥结构数据库中正式报告的 100 000 多个 MOF 的合成。为了扫描如此大量的 MOF，计算高通量筛选 (HTS) 已成为识别给定应用最有希望的结构和/或发现有用的结构-性质关系的常用方法。然而，尽管进行了所有这些数据挖掘工作，但只有一小部分 HTS 研究确定了可合成的性能最佳的 MOF，然后在实验室中对其进行了进一步研究。从这个角度来看，