Metal–organic frameworks (MOFs) have been widely investigated for challenging catalytic transformations due to their well-defined structures and high degree of synthetic tunability. These features, at least in principle, make MOFs ideally suited for a computational approach towards catalyst design and discovery. Nonetheless, the widespread use of data science and machine learning to accelerate the discovery of MOF catalysts has yet to be substantially realized. In this review, we provide an overview of recent work that sets the stage for future high-throughput computational screening and machine learning studies involving MOF catalysts. This is followed by a discussion of several challenges currently facing the broad adoption of data-centric approaches in MOF computational catalysis, and we share possible solutions that can help propel the field forward.

中文翻译：

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实现金属有机框架催化剂的数据驱动、计算发现

金属有机框架（MOF）由于其明确的结构和高度的合成可调性而被广泛研究用于具有挑战性的催化转化。至少在原则上，这些特征使 MOF 非常适合用于催化剂设计和发现的计算方法。尽管如此，广泛使用数据科学和机器学习来加速 MOF 催化剂的发现尚未得到实质性实现。在这篇综述中，我们概述了最近的工作，这些工作为未来涉及 MOF 催化剂的高通量计算筛选和机器学习研究奠定了基础。随后讨论了目前在 MOF 计算催化中广泛采用以数据为中心的方法面临的几个挑战，