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<p>Hydrogen is a promising clean energy carrier, but its low energy density necessitates advanced storage solutions. Metal–Organic Frameworks (MOFs) offer high tunability and porosity for efficient hydrogen adsorption. This work combines Grand Canonical Monte Carlo (GCMC) simulations with machine learning, employing Feed-Forward (FNN) and Pattern Recognition (PRNN) neural networks optimized via Equilibrium Optimizer and Genetic Algorithm. The integrated approach predicts gravimetric and volumetric hydrogen storage capacities across 98,695 metal–organic frameworks under temperature–pressure swing conditions. Pore volume and void fraction emerged as dominant structural descriptors. The models identified 12 top-performing MOFs exceeding MOF-5 in both gravimetric (8.27 wt.%) and volumetric (51.94 g-H<sub>2</sub>/L) capacities, demonstrating the power of ML-accelerated screening for next-generation hydrogen storage materials.</p>

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Accelerating discovery of MOFs for hydrogen storage via machine learning in energy related applications

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月18日

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  • Saeid Khairandesh,
  • Marzieh Lotfi,
  • Afsanehsadat Larimi,
  • Ali Akbar Asgharinezhad,
  • Cyrus Ghotbi

摘要

Hydrogen is a promising clean energy carrier, but its low energy density necessitates advanced storage solutions. Metal–Organic Frameworks (MOFs) offer high tunability and porosity for efficient hydrogen adsorption. This work combines Grand Canonical Monte Carlo (GCMC) simulations with machine learning, employing Feed-Forward (FNN) and Pattern Recognition (PRNN) neural networks optimized via Equilibrium Optimizer and Genetic Algorithm. The integrated approach predicts gravimetric and volumetric hydrogen storage capacities across 98,695 metal–organic frameworks under temperature–pressure swing conditions. Pore volume and void fraction emerged as dominant structural descriptors. The models identified 12 top-performing MOFs exceeding MOF-5 in both gravimetric (8.27 wt.%) and volumetric (51.94 g-H2/L) capacities, demonstrating the power of ML-accelerated screening for next-generation hydrogen storage materials.