Predicting methane adsorption ability of MOFs based on the crystal structural information by convolutional neural network

High-throughput computational screening (HTCS) methods have been widely used in addressing the challenges of screening metal-organic framework (MOF) materials with high performance from large datasets. Accurately predicting the performance of MOFs with hypothetical structures, such as adsorption capacity for methane, can be a great help to HTCS. Traditional molecular dynamics simulation methods are computationally cumbersome and costly. Feature engineering-based machine learning methods have a high reliance on expert experience to extract effective features, some of which can be time-consuming to calculate. Although some classical machine learning models can already achieve good performance, the heavy burden for features computation prior to model training would eventually limit their practical applications, flexibility and generality. In this paper, an approach is proposed for predicting the methane adsorption ability of MOFs directly from their crystal structural information recorded in Crystallographic Information File (CIF)-formatted files, without the need for molecular dynamics simulation or calculating any physical or chemical properties. By expressing the structural information in the crystal cells of MOFs as matrices, a deep convolutional neural network model based on residual structure is applied to train an end-to-end model to predict their methane adsorption ability. The experimental results show that the model accomplishes the prediction task with high accuracy, giving the mean square correlation coefficient (R²) as 0.925 and the mean absolute error (MAE) as 22.28 cm³g^-1, which demonstrates a good performance over baselines.