Method for high-throughput screening of metal organic framework membrane based on machine learning assistance

The invention discloses a method for high-throughput screening of a metal organic framework membrane based on machine learning assistance. The method comprises the following steps: collecting geometric structure characteristic parameters and descriptor parameters of a metal organic framework material; establishing a data set based on the geometric structure characteristic parameters and the descriptor parameters; dividing a data set into a training set and a test set, and performing standardized preprocessing on the data; modeling and evaluating the prediction capability of the model; selecting an optimal model algorithm, and iteratively adjusting hyper-parameters of the model until the accuracy of the model for predicting the structural features of the MOFs material meets the precision requirement; and taking descriptor parameters in the test set as model input, predicting structural features corresponding to data in the test set by using the trained model, calculating prediction accuracy, and quantitatively