Prediction of methane storage in covalent organic frameworks using big-data-mining approach

Abstract

Abstract A combination of computational materials screening and machine learning (ML) technique is being adopted as a popular approach to study various materials toward application of interest. In this work, we began with high-throughput molecular simulations to calculate the methane storage (6.5 MPa) and deliverable (6.5-0.58 MPa) capacities of 404,460 covalent organic frameworks (COFs) at 298 K. Then, the full data sets with 23 features were randomly split into training and test sets in a ratio of 20:80, which were applied to evaluate the prediction abilities of several ML algorithms, including gradient boosting decision tree (GBDT), neural network (NN), support vector machine (SVM), random forest (RF) and decision tree (DT). The results indicate that the RF model has the highest prediction accuracy, which was further employed to reduce the dimension of features space and quantitatively analyze the relative importance of each feature value. The binary classification predictors built using the features with the highest influence weight can give a successful identification of top-performing candidates from the test set containing 323,168 COFs with an accuracy exceeding 96%. The deliverable capacities of the identified COFs were found to outperform those reported so far for various adsorbents. The findings may provide a useful guidance for the design and synthesis of new high-performance materials for methane storage application.