Metal organic framework screening method for capturing low-concentration carbon dioxide in air

The invention relates to the technical field of computational chemistry, gas adsorption and energy conservation and environmental protection, in particular to a metal organic framework screening method for capturing low-concentration carbon dioxide in air, which is based on high-flux calculation and a machine learning system and comprises the following steps: simulating the adsorption characteristic and diffusion characteristic of MOF (Metal Organic Framework) to CO2 in low-concentration CO2 by utilizing high-flux calculation; predicting adsorption selectivity by using four machine learning algorithms; and verifying the random forest model by using five-fold cross validation, and quantitatively analyzing the importance of each descriptor of the MOF by using a random forest algorithm. Compared with a traditional artificial experiment, the method for screening the MOF for capturing the low-concentration carbon dioxide in the air has the advantages that Zeo + + and RASPA programs are used for obtaining structure