Prediction of hydrogen storage in metal-organic frameworks: A neural network based approach

Abstract

Gas capture, sensing, and storage systems are all within the capabilities of metal–organic frameworks (MOFs). It is common practise to choose the MOF with the best adsorption property from a large database before running an adsorption calculation. High-throughput computational research is sometimes hampered by the expense of computing thermodynamic values, slowing the progress of MOFs for separations and storage applications. When trying to predict material properties, machine learning has recently emerged as a possible alternative to more conventional methods like experiments and simulations. The H2 capacities of 918,734 MOFs drawn from 19 databases were recently predicted using ML by Ahmed and Siegel (2021). Several ML methods were utilized, and the extremely randomised tree (ERT) model emerged as the most accurate predictor of hydrogen delivery capacity in terms of both gravimetric and volumetric quantities. Interestingly we have used deep learning model (Feed-forward neural network) as well as ERT model for the prediction of H2 deliverable capacities of a huge number of MOFs developed from the previous studies and got till date best results for predictions. To verify our model’s efficacy, we also performed Grand Canonical Monte Carlo (GCMC) simulations. We show our method by forecasting the hydrogen storage capacity of MOFs during a temperature and pressure swing from 100bar/77K to 5bar/160K.

Keywords

• MOFs
• Hydrogen storage
• Deep learning
• Neural network
• GCMC