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

Key Points

• The aim is to develop a machine learning-based approach for optimizing the pore structure of metal-organic frameworks to improve methane storage capacity.
• Utilized machine learning algorithms to guide the pore engineering process.
• Designed and tested various metal-organic frameworks as methane adsorbents.
• Conducted performance comparisons with previously reported frameworks.
• Achieved unprecedented methane storage capacity, surpassing existing metal-organic frameworks.
• Demonstrated that the ML-guided approach can effectively improve adsorbent design.