Unlocking Hydrogen's Potential: Prediction of Adsorption in Metal-organic Frameworks for Sustainable Energy Storage

Accurately predicting hydrogen adsorption behavior is essential to developing efficient materials with storage capacities approaching those of liquid hydrogen and surpassing the performance of conventional compressed gas storage systems. Grand canonical Monte Carlo (GCMC) simulations accurately predict adsorption isotherms but are computationally expensive, limiting large-scale material screening. We employ GPU-accelerated threedimensional classical density functional theory (DFT) based on the SAFT-VRQ Mie equation of state with a first-order Feynman–Hibbs correction to model hydrogen adsorption in [Zn(bdc)(ted)0.5], MOF-5, CuBTC, and ZIF-8 at 30 K, 50 K, 77 K, and 298 K. Our approach generates adsorption isotherms in seconds compared to hours for GCMC simulations, with quantum corrections proving crucial for accurate low-temperature predictions. The results show good agreement with GCMC simulations and available experiments, demonstrating classical DFT as a powerful tool for high-throughput material screening and optimizing hydrogen storage applications.