Park Junkil, Kim Honghui, Kang Yeonghun, Lim Yunsung, Kim Jihan
Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.
JACS Au. 2024 Sep 12;4(10):3727-3743. doi: 10.1021/jacsau.4c00618. eCollection 2024 Oct 28.
Renowned for their high porosity and structural diversity, metal-organic frameworks (MOFs) are a promising class of materials for a wide range of applications. In recent decades, with the development of large-scale databases, the MOF community has witnessed innovations brought by data-driven machine learning methods, which have enabled a deeper understanding of the chemical nature of MOFs and led to the development of novel structures. Notably, machine learning is continuously and rapidly advancing as new methodologies, architectures, and data representations are actively being investigated, and their implementation in materials discovery is vigorously pursued. Under these circumstances, it is important to closely monitor recent research trends and identify the technologies that are being introduced. In this Perspective, we focus on emerging trends of machine learning within the field of MOFs, the challenges they face, and the future directions of their development.
金属有机框架（MOF）以其高孔隙率和结构多样性而闻名，是一类在广泛应用中颇具前景的材料。近几十年来，随着大规模数据库的发展，MOF领域见证了数据驱动的机器学习方法带来的创新，这些方法使人们能够更深入地理解MOF的化学本质，并推动了新型结构的开发。值得注意的是，随着新方法、架构和数据表示方式的积极研究以及它们在材料发现中的大力应用，机器学习正在持续快速发展。在这种情况下，密切关注近期研究趋势并确定正在引入的技术非常重要。在这篇综述中，我们关注MOF领域内机器学习的新兴趋势、它们面临的挑战以及未来的发展方向。
