Interpretable Graph Transformer Network for Predicting Adsorption Isotherms of Metal-Organic Frameworks

Predicting interactions between metal-organic frameworks (MOFs) and their adsorbates based on structures is critical to design high-performance porous materials. Many gas uptake prediction models have been proposed, but adsorption isotherm prediction is still challenging for most existing models. Here, we report a deep learning approach (MOFNet) that can predict adsorption isotherms for MOFs based on hierarchical representation and pressure adaptive mechanism. We elaborately design a hierarchical representation to encode the MOF structures. We adopt a graph transformer network to capture atomic-level information, which can help learn chemical features required under low-pressure conditions. A pressure adaptive mechanism is employed to interpolate and extrapolate the given limited data points by transfer learning, which can predict adsorption isotherms on a wider pressure range by only one model. We demonstrate that our predictor outperformed other traditional machine learning as well as graph neural network models on the challenging benchmarks and also achieves high performance on the real-world experimental observed adsorption isotherms. Finally, we interpret the models to discover and present potential structure-property relationships using the self-attention mechanism in the network. The proof-of-concept applications, such as disordered MOF predictions and missing data imputation of gas adsorption isotherms, showcase the generality and usability of our model to improve MOF material design.