Machine learning-guided pore engineering enhances methane storage capacity in metal-organic frameworks, suggesting innovative design approaches.
, surpassing all previously reported MOFs. This ML-guided pore engineering paradigm provides a generalizable route for the rational design of high-performance methane adsorbents.
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Song et al. (2026) studied this question.
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