Application of machine learning in MOFs for gas adsorption and separation

Abstract

Metal-organic frameworks (MOFs) with high specific surface area, permanent porosity and extreme modifiability had great potential for gas storage and separation applications. Considering the theoretically nearly infinite variety of MOFs, it was difficult but necessary to achieve high-throughput computational screening (HTCS) of high-performance MOFs for specific applications. Machine learning (ML) was a field of computer science where one of its research directions was the effective use of information in a big data environment, focusing on obtaining hidden, valid and understandable knowledge from huge amounts of data, and had been widely used in materials research. This paper firstly briefly introduced the MOFs databases and related algorithms for ML, followed by a detailed review of the research progress on HTCS of MOFs based on ML according to four classes of descriptors, including geometrical, chemical, topological and energy-based, for gas storage and separation, and finally a related outlook was presented. This paper aimed to deepen readers’ understanding of ML-based MOF research, and to provide some inspirations and help for related research.