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Related Experiment Video

Updated: May 18, 2026

Determining Surface Areas and Pore Volumes of Metal-Organic Frameworks
06:45

Determining Surface Areas and Pore Volumes of Metal-Organic Frameworks

Published on: March 8, 2024

Machine Learning-Guided Pore Engineering of Metal-Organic Frameworks for Ultrahigh Volumetric Methane Storage.

Mengyao Song1, Rui Gao1, Jieqiu Huang1

  • 1Engineering Research Center of Photoenergy Utilization for Pollution Control and Carbon Reduction, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, Hubei 430079, China.

Journal of the American Chemical Society
|May 16, 2026
PubMed
Summary

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Machine learning accelerates the discovery of metal-organic frameworks (MOFs) for efficient methane storage. This strategy optimizes MOFs for high volumetric methane working capacity, advancing adsorbed natural gas technologies.

Area of Science:

  • Materials Science
  • Chemical Engineering
  • Computational Chemistry

Background:

  • Adsorbed natural gas (ANG) technologies require materials with high volumetric methane storage capacity under mild conditions.
  • Metal-organic frameworks (MOFs) are promising candidates due to their tunable structures and high surface areas.
  • Developing efficient MOFs for methane storage remains a significant challenge.

Purpose of the Study:

  • To develop and apply a machine learning (ML)-guided strategy for identifying and optimizing MOFs for high volumetric methane working capacity.
  • To integrate computational screening with experimental pore engineering for MOF design.
  • To establish a generalizable paradigm for designing high-performance methane adsorbents.

Main Methods:

  • A machine learning model (Extra Trees Regressor) was trained using adsorption data from grand canonical Monte Carlo simulations.

More Related Videos

Synthesis and Characterization of Functionalized Metal-organic Frameworks
11:27

Synthesis and Characterization of Functionalized Metal-organic Frameworks

Published on: September 5, 2014

Related Experiment Videos

Last Updated: May 18, 2026

Determining Surface Areas and Pore Volumes of Metal-Organic Frameworks
06:45

Determining Surface Areas and Pore Volumes of Metal-Organic Frameworks

Published on: March 8, 2024

Synthesis and Characterization of Functionalized Metal-organic Frameworks
11:27

Synthesis and Characterization of Functionalized Metal-organic Frameworks

Published on: September 5, 2014

  • Large-scale computational screening of MOFs from the CoRE MOF 2024 database was performed.
  • Experimental pore engineering, including linker functionalization, was used to optimize selected MOFs.

    • Main Results:

    • The ML model achieved high predictive accuracy (R² = 0.96) for volumetric methane working capacity.
    • UMCM-4, MUF-8, and MOF-5 were identified as top-performing MOFs, with experimental capacities matching predictions.
    • Optimal ranges for porosity, pore volume, and framework density were determined for methane storage.
    • Functionalized MUF-8 variants (MUF-8-CH₃ and MUF-8-C₄H₄) achieved record volumetric methane working capacities (237 cm³(stp) cm⁻³).

    Conclusions:

    • The ML-guided pore engineering strategy is effective for discovering and optimizing MOFs for methane storage.
    • Key structural parameters like porosity, pore volume, and framework density critically influence volumetric methane storage.
    • This approach offers a generalizable route to design advanced materials for adsorbed natural gas applications.