Core Insights - The 2025 Nobel Prize in Chemistry was awarded to researchers S. Kitagawa, Richard Robson, and Omar Yaghi for their contributions to the field of Metal-Organic Frameworks (MOFs), marking a significant milestone in over 30 years of research and development in this area [1][2][11] - The advancements in MOF research have transitioned from structural design to industrial applications, with artificial intelligence (AI) now playing a crucial role in reshaping the field [1][12] Group 1: Historical Development of MOFs - Richard Robson proposed the concept of three-dimensional coordination polymers in 1989, which laid the groundwork for the development of MOFs [3] - Over the next 15 years, Omar Yaghi and S. Kitagawa made significant breakthroughs in structural construction and functional regulation, establishing MOFs as a new class of porous materials [3][4] - The introduction of flexible frameworks and tunable pores by S. Kitagawa transformed MOFs from rigid materials to dynamic structures, enhancing their applicability [4] Group 2: Industrial Applications and Innovations - MOFs have shown potential in various applications, including gas storage, carbon capture, and biomedical fields, with commercial structures like the Zr-based UiO series being developed for high thermal stability [8][10] - The CALF-20 MOF, developed by the University of Calgary, has been utilized for carbon capture in cement production, demonstrating the material's effectiveness in challenging environments [10][11] Group 3: AI Integration in MOF Research - The integration of AI in MOF research has led to significant advancements, with a notable increase in publications on the topic since 2016, indicating a growing interest in the intersection of AI and MOFs [12][14] - Recent developments include the MOFFlow model, designed specifically for predicting MOF structures, and the MOFGen system, which utilizes various AI techniques for generating and validating MOF structures [21][24][26] - The modular and parameterizable nature of MOFs makes them ideal candidates for AI-driven research, allowing for a more systematic approach to material discovery and design [16][18]