White-box methodologies for achieving robust correlations in hydrogen storage with metal-organic frameworks

Area of Science:

• Materials Science
• Chemical Engineering
• Computational Chemistry

Background:

• Hydrogen is a key clean energy carrier.
• Metal-organic frameworks (MOFs) are promising for hydrogen storage.
• Accurate prediction of hydrogen storage capacity is crucial.

Purpose of the Study:

• To estimate hydrogen storage in MOFs using soft-computing methods.
• To develop robust correlations for predicting hydrogen storage capacity.
• To identify key parameters influencing hydrogen storage in MOFs.

Main Methods:

• Utilized white-box methods: Group Method of Data Handling (GMDH), Genetic Programming (GP), and Gene Expression Programming (GEP).
• Employed temperature, pressure, pore volume, and surface area as input parameters.
• Performed statistical and graphical error assessment and sensitivity analysis.

Main Results:

• The GMDH model achieved the highest accuracy (RMSE: 0.410, MAE: 0.307), with GEP showing comparable performance.
• Pore volume and pressure were identified as the strongest linear and non-linear predictors, respectively.
• Temperature exhibited a minimal negative impact on hydrogen storage.

Conclusions:

• Developed accurate predictive models for hydrogen storage in MOFs.
• Pore volume and pressure are critical factors for optimizing MOF-based hydrogen storage.
• The models demonstrate high statistical reliability, with 96% of data within acceptable regions.