Broad Range Material-to-System Screening of Metal-Organic Frameworks for Hydrogen Storage Using Machine Learning

Hydrogen is pivotal in the transition to sustainable energy systems, serving essential roles in both power generation and industrial applications. Metal-organic frameworks have emerged as promising candidates for efficient hydrogen storage. However, identifying promising candidates for deployment is challenging due to the vast number of synthesized MOFs. This study integrates molecular simulations, machine learning, and techno-economic analysis to evaluate the performance of MOFs across broad operation conditions for different scales and durations of hydrogen storage applications. While previous screenings of the MOF database have predominantly emphasized high hydrogen capacities under cryogenic conditions, we identify that optimal temperatures and pressures for cost minimization depend on the raw price of the MOF. Specifically, when MOFs are priced at $15/kg, 99.72% exhibit optimal cost-efficiency within a temperature range of 180 K to 250 K and 98.9% achieve this at pressure of 150 bar. Furthermore, we characterize correlations between system cost and material properties, identifying key promising features of MOFs for low-cost systems including low densities (<1 g/cm³), high surface areas (>1500 m²/L), large void fractions (>0.6), and high pore volumes (>0.8 cm³/g).