Machine Learning-Guided Pore Engineering of Metal–Organic Frameworks for Ultrahigh Volumetric Methane Storage
, surpassing all previously reported MOFs. This ML-guided pore engineering paradigm provides a generalizable route for the rational design of high-performance methane adsorbents.
Authors
- Binbin Tu (ORCID: https://orcid.org/0000-0003-3800-6208)
- Chongchong Hou
- Y. Li (ORCID: https://orcid.org/0009-0006-9970-5984)
- Rui Gao
- Kaiqi Wang
- Honghui Lei (ORCID: https://orcid.org/0009-0008-5468-3894)
- Jieqiu Huang
- Yanbing Guo (ORCID: https://orcid.org/0000-0002-5399-1739)
- Chao Shu (ORCID: https://orcid.org/0000-0002-4722-2714)
- Huiling Wang
- Mengyao Song
- Qingqing Pang (ORCID: https://orcid.org/0000-0003-0580-8570)
- Zhu Luo (ORCID: https://orcid.org/0000-0002-4766-3499)
Institutions
- Central China Normal University (CN)
- Ministry of Education (IR)
- Wuhan University of Science and Technology (CN)
- Wuhan Institute of Technology (CN)
Publication Details
- Journal
- Journal of the American Chemical Society
- Published
- 2026-05-16
- DOI
- https://doi.org/10.1021/jacs.6c02540
- Primary Topic
- Metal-Organic Frameworks: Synthesis and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Hubei Province
- Fundamental Research Funds for the Central Universities