As an emerging class of nanoporous materials, metal–organic frameworks (MOFs) have the advantages of designability and structural and functional tunability, compared with traditional porous materials, which are widely used in various fields. The structural adjustability of MOFs provides the possibility of infinite material generation and a huge material space. At present, tens of thousands of MOFs have been synthesized and the number continues to grow at an alarming rate, which makes it difficult to explore the application prospects of all materials only by traditional experimental methods. Therefore, more efficient alternative methods are urgently needed to identify and screen MOFs. As a powerful data analysis tool, machine learning (ML) has shown great potential in the materials field, which can intuitively and quickly analyze the structure–property relationship and guide the rational design and preparation of reticular materials such as MOFs. This review systematically presents the complete workflow and cutting-edge developments in ML applications in the field of MOF research covering data preparation, algorithm selection, model evaluation, model optimization and application status. Further, rational design methods and future challenges are discussed. This review aims to provide the new paradigm of the combination of ML and MOFs and promote ML applied in MOF research efficiently.

中文翻译：

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MOF 中机器学习应用的全面概述：从建模过程到最新应用和设计分类


金属有机框架 （MOFs） 作为一类新兴的纳米多孔材料，与传统的多孔材料相比，具有可设计性、结构和功能可调性等优势，被广泛应用于各个领域。MOF 的结构可调性提供了无限材料生成的可能性和巨大的材料空间。目前，已经合成了数以万计的 MOFs，并且数量继续以惊人的速度增长，这使得仅靠传统的实验方法难以探索所有材料的应用前景。因此，迫切需要更有效的替代方法来识别和筛选 MOFs。机器学习 （ML） 作为一种强大的数据分析工具，在材料领域显示出巨大的潜力，它可以直观、快速地分析结构-性能关系，指导 MOF 等网状材料的合理设计和制备。本文系统介绍了 MOF 研究领域 ML 应用的完整工作流程和前沿发展，包括数据准备、算法选择、模型评估、模型优化和应用状态。此外，讨论了合理的设计方法和未来的挑战。本文旨在提供 ML 与 MOF 结合的新范式，并有效促进 ML 在 MOF 研究中的应用。