Computational Exploration of Adsorption-Based Hydrogen Storage in Mg-Alkoxide Functionalized Covalent-Organic...

Area of Science:

• Materials Science
• Computational Chemistry
• Energy Storage

Background:

• Hydrogen is a clean energy carrier, but efficient storage remains a significant challenge.
• Current hydrogen storage methods (high-pressure compression or cryogenic liquefaction) are energy-intensive and costly.
• Developing safe, reliable, and energy-efficient storage solutions at lower pressures and temperatures is essential.

Purpose of the Study:

• To computationally design and screen a large database of Mg-alkoxide-functionalized covalent-organic frameworks (COFs) for hydrogen storage.
• To develop accurate computational models for predicting hydrogen binding energies and storage capacities.
• To evaluate the gravimetric and volumetric hydrogen storage performance of functionalized COFs across various temperatures.

Main Methods:

• Systematic design of 2902 Mg-alkoxide-functionalized COFs.
• High-throughput (HT) computational screening using MP2 calculations for binding energies and fitting modified-Morse force field (FF) parameters.
• HT grand canonical Monte Carlo (GCMC) simulations for hydrogen uptake calculations and machine learning (ML) models for performance prediction.

Main Results:

• Mg-alkoxide functionalization significantly enhances volumetric hydrogen storage capacity, particularly in smaller-pore COFs.
• Developed ML models accurately predict gravimetric (MAE: 0.061 wt %) and volumetric (MAE: 0.456 g/L) deliverable capacities.
• Screening identified promising COF structures for efficient hydrogen storage at 111, 231, and 296 K.

Conclusions:

• Mg-alkoxide functionalization is an effective strategy for improving hydrogen storage in COFs.
• The developed computational and ML approaches enable rapid screening of novel materials for hydrogen storage.
• This work provides a pathway for designing advanced materials for safe and efficient hydrogen energy applications.