Finding the optimal CO2 adsorption material: Prediction of multi-properties of metal-organic frameworks (MOFs) based on DeepFM

Metal-organic frameworks (MOFs) has been widely considered as promising candidates for CO2 adsorption due to their high porosity and high structural adjustability. By combining the various properties of MOFs, an MOFs material-property matrix can be obtained to find the best MOFs for different applications, but this matrix is often incomplete in practice. In this work, a DeepFM model was developed to predict the multi properties of CO2 absorbents with limited start-up training data, but with high prediction accuracy. The DeepFM model contains deep neural network, with better non-linear fitting ability, thus significantly improved the prediction accuracy. Meanwhile, by adding the descriptors of MOFs as the input data of new features, the DM model also alleviates the cold start problem. By predicting 28 adsorption properties of 8206 screened hypothetical MOFs, 7 high-performance MOFs for CO2 adsorption were selected. In addition, the model also helps to find out the relative importance degree of various descriptors on the CO2 capture capabilities of MOFs, which is of great help in future MOFs synthesis.