Machine Learning Screening of MOF Materials for Gas Adsorption Separation
Metal-organic framework(MOF)is widely used in the field of gas adsorption separation due to their high porosity,high specific surface area and structural adjustability.As the number of MOF surges,traditional molecular simulation and experimental methods are costly and slow to verify the performance of MOF,so the current MOF screening efforts have turned to high-throughput computation-assisted machine learning(ML).As an efficient big data processing method,ML can fit data on the basis of high-throughput computational screening(HTCS),so as to quickly and accurately screen out gas adsorption separation materials,and deeply explore the relationship between their structure and performance.This paper reviews recent studies on the application of ML to MOF screening.In this paper,we focus on some work using ML to select MOF materials from a large number of structures that can be used for adsorption,separation and storage of CH4,H2 and CO2.Moreover,we review the current research ideas and progress in MOF material screening,and point out some bottlenecks and challenges faced by ML in the screening of MOF materials.Finally,the future development prospect of this field is prospected.
万方数据
维普
