Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation

Abstract

The acceleration in design of new metal organic frameworks (MOFs) has led scientists to focus on high-throughput computational screening (HTCS) methods to quickly assess the promises of these fascinating materials in various applications. HTCS studies provide a massive amount of structural property and performance data for MOFs, which need to be further analyzed. Recent implementation of machine learning (ML), which is another growing field in research, to HTCS of MOFs has been very fruitful not only for revealing the hidden structure–performance relationships of materials but also for understanding their performance trends in different applications, specifically for gas storage and separation. In this review, we highlight the current state of the art in ML-assisted computational screening of MOFs for gas storage and separation and address both the opportunities and challenges that are emerging in this new field by emphasizing how merging of ML and MOF simulations can be useful.

Research progress in metal–organic frameworks (MOFs) in CO2capture from post-combustion coal-fired flue gas: characteristics, preparation, modification and applications

“…However, in the face of such a huge search space, it is actually impossible to screen the MOFs by only using single-point DFT calculations and GCMC simulations. 170,171 In recent years, machine learning (ML) has gradually become a powerful means of high-throughput screening, which is a collective term for advanced statistical tools and algorithms used to classify, predict, optimize, and cluster data. The ML is mainly composed of three parts, namely collecting data sets, preparing and analyzing descriptors as input variables, and training algorithms (such as decision trees, support vector machines, neural networks, random forests, etc.…”

“…As an emerging field in MOF screening, ML still has some deficiencies and points worthy of breakthrough. 170–172 The ML is a data-driven technology, and its performance usually depends on the size and quality of the database. It is necessary to establish an up-to-date, accurate, consistent and comprehensive database, and combine and examine MOFs from different databases in the ML studies to promote the discovery of common laws.…”

Metal–Organic Framework-Based Materials for Adsorption and Detection of Uranium(VI) from Aqueous Solution

“…Therefore, MOFs are of interest for gas storage and separation, catalysis, water purification and remediation, or sensing small molecules for environment pollutants, etc. 38 − 41 …”

Application of Fiber Biochar–MOF Matrix Composites in Electrochemical Energy Storage

“…Other materials include organic semiconductors, inorganic metal complexes, organic metal complexes, etc. MOFs display excellent properties such as high porosity, an adjustable pore structure, and a large specific surface area; MOFs can be applied to the fields of gas separation and storage [21,22], catalysis [23], and electrochemical energy storage [24]. However, MOFs have the problems of structural instability and low conductivity [25], which limit their application in energy storage.…”