Efficient Removal of Greenhouse Gases: Machine Learning-Assisted Exploration of Metal-Organic Framework Space.

Abstract

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.