Deep-Learning-Based End-to-End Predictions of CO₂ Capture in Metal-Organic Frameworks

Metal-organic frameworks (MOFs) have become an active topic because of their excellent carbon capture and storage (CCS) properties. However, it is quite challenging to identify MOFs with superior performance within a massive combinatorial search space. To this end, we propose a deep-learning-based end-to-end prediction model to rapidly and accurately predict the CO₂ working capacity and CO₂/N₂ selectivity of a given MOF under low-pressure conditions. Different from previous methods, our prediction model relies only on the data from the Crystallographic Information File (CIF) rather than handcrafted geometric descriptors and chemical descriptors. The model was developed, trained, and tested on a dataset of 342489 topologically diverse MOFs. Experimental results on the dataset show that the proposed model achieves high prediction performance, i.e., R² = 0.916 for predicting the CO₂ working capacity and R² = 0.911 for predicting the CO₂/N₂ selectivity. With regard to the identification of potential high-performing MOFs, 1020 of 1027 (top 3%) high-performance MOFs were recovered while screening only 12% of the entire dataset using our provided pretrained model, reducing the computation time by nearly an order of magnitude when the model was used to prescreen material prior to computationally intensive grand canonical Monte Carlo (GCMC) simulations while still capturing 99% of the high-performance MOFs. In the ab initio training task, the method can achieve R² = 0.85 with only 20% of the labeled data used for training and recover 995 of 1027 (top 3%) high-performance MOFs with only 12% of the entire dataset screened.