A multi-objective optimization-driven screening approach for maximizing hydrogen storage capacities in MOFs.

Area of Science:

• Materials Science
• Chemical Engineering
• Computational Chemistry

Background:

• Metal-organic frameworks (MOFs) present a tradeoff between gravimetric and volumetric hydrogen storage capacities.
• Existing MOFs show performance limitations at elevated temperatures (298 K), hindering commercial applications.

Purpose of the Study:

• To develop a computational framework for identifying MOFs with superior hydrogen storage capabilities.
• To overcome the temperature-dependent performance limitations of current MOFs for hydrogen storage.

Main Methods:

• Utilized a multi-objective particle swarm optimization framework guided by bootstrapped-random forest predictions.
• Screened 733,792 existing structures against 152 theoretical MOF feature combinations.
• Employed Grand Canonical Monte Carlo and molecular dynamics simulations for performance validation.

Main Results:

• Identified 43 promising MOFs, with Zn-based MOF-2087 identified as the top performer.
• MOF-2087 demonstrated consistent hydrogen storage performance across temperatures.
• Achieved high hydrogen uptake in MOF-2087 (5.3 wt% and 7.4 gH2 L-1 at 298 K).

Conclusions:

• MOF-2087 is a computationally promising material for hydrogen storage up to 298 K.
• Optimization-driven screening strategies are effective for discovering advanced MOFs.
• Identified C-clusters and metal sites as crucial for enhanced hydrogen adsorption in MOF-2087.