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引用次数: 0
摘要
全球变暖是人类必须共同面对的危机。温室气体(GHGs)是导致全球变暖的主要因素,因此采用相关工艺消除温室气体至关重要。金属有机框架(MOFs)具有高比表面积、大孔隙率和可调合成等优点,在温室气体储存、吸附、分离和催化等方面备受关注。然而,随着新合成和新发现的出现,MOFs 的数量迅速增加,为特定应用找到合适的 MOFs 极具挑战性。在这方面,高通量计算筛选被认为是筛选大量材料以发现高性能目标 MOF 的最有效研究方法。通常,高通量计算筛选会产生大量多维数据,非常适合机器学习(ML)训练,以提高筛选效率并深入探索多维数据之间的关系。本综述总结了在温室气体去除领域使用 ML 筛选 MOFs 的一般流程和常用方法。同时还探讨了 ML 在探索 MOF 空间时所面临的挑战,以及未来 ML 在 MOF 筛选方面的潜在发展方向。旨在加深对 ML 与 MOFs 在各领域结合的理解,拓宽 MOFs 的应用和发展思路。
Efficient Removal of Greenhouse Gases: Machine Learning-Assisted Exploration of Metal-Organic Framework Space.
Global warming is a crisis that humanity must face together. With greenhouse gases (GHGs) as the main factor causing global warming, the adoption of relevant processes to eliminate them is essential. With the advantages of high specific surface area, large pore volume, and tunable synthesis, metal-organic frameworks (MOFs) have attracted much attention in GHG storage, adsorption, separation, and catalysis. However, as the pool of MOFs expands rapidly with new syntheses and discoveries, finding a suitable MOF for a particular application is highly challenging. In this regard, high-throughput computational screening is considered the most effective research method for screening a large number of materials to discover high-performance target MOFs. Typically, high-throughput computational screening generates voluminous and multidimensional data, which is well suited for machine learning (ML) training to improve the screening efficiency and explore the relationships between the multidimensional data in depth. This Review summarizes the general process and common methods for using ML to screen MOFs in the field of GHG removal. It also addresses the challenges faced by ML in exploring the MOF space and potential directions for the future development of ML for MOF screening. This aims to enhance the understanding of the integration of ML and MOFs in various fields and broaden the application and development ideas of MOFs.
期刊介绍:
ACS Nano, published monthly, serves as an international forum for comprehensive articles on nanoscience and nanotechnology research at the intersections of chemistry, biology, materials science, physics, and engineering. The journal fosters communication among scientists in these communities, facilitating collaboration, new research opportunities, and advancements through discoveries. ACS Nano covers synthesis, assembly, characterization, theory, and simulation of nanostructures, nanobiotechnology, nanofabrication, methods and tools for nanoscience and nanotechnology, and self- and directed-assembly. Alongside original research articles, it offers thorough reviews, perspectives on cutting-edge research, and discussions envisioning the future of nanoscience and nanotechnology.