AI助力设计可持续的金属有机框架水捕获材料
Sustainable Metal-Organic Framework Water Harvesters in the Artificial Intelligence Era
- 结合人工智能优化MOF结构以提升吸水性能
- 多变量策略与长臂链接器增强吸附容量和稳定性
- 适合关注智能材料设计与可持续水收集的研究者
金属-有机框架（MOFs）因其可调的孔隙环境，是干旱条件下捕获与释放水的优良候选材料。将人工智能（AI）融入MOF发现过程，可通过识别增强大气水捕获（AWH）、稳定性及循环效率的结构特征，进一步加速高性能吸附剂的设计。本文综述了关键的MOF设计原则，包括协同吸附、操作相对湿度（RH）、吸水量、滞后效应与可扩展性。重点介绍了多变量策略与长臂链接器延伸等最新进展，阐明这些设计如何调控孔隙容量与亲水性，同时保持结构稳定性和结晶度。此外，探讨了AI、大语言模型（LLMs）与数据挖掘在预测合成、逆向设计以及揭示合成-结构-性能关系中的作用，推动下一代MOF水捕获材料的发展。
原文摘要 · Abstract (English)
Metal-organic frameworks (MOFs) are excellent candidates for water harvesting due to their tunable pore environments, which can be precisely engineered to capture and release water in arid conditions. Integrating artificial intelligence (AI) into MOF discovery can further accelerate the design of high-performance sorbents by identifying structural features that enhance atmospheric water harvesting (AWH), stability, and cycling efficiency. In this Perspective, we examine key MOF design principles, including cooperative adsorption, operational relative humidity (RH), uptake capacity, hysteresis, and scalability. We highlight recent design advancements such as multivariate strategies and long-arm linker extension, and examine how these principles tune pore capacity and hydrophilicity, while preserving stability and crystallinity. Furthermore, we discuss how AI, large language models (LLMs), and data mining can accelerate the discovery process through predictive synthesis, inverse design, and elucidating synthesis-structure-property relationships for the next generation of MOF water harvesters.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。