Computational Screening of Trillions of Metal–Organic Frameworks for High-Performance Methane Storage
Abstract
In the past decade, there has been an increasing number of computational screening works to facilitate finding optimal materials for a variety of different applications. Unfortunately, most of these screening studies are limited to their initial set of materials and result in a brute-force type of screening approach. In this work, we present a systematic strategy that can find metal–organic frameworks (MOFs) with the desired properties from an extremely diverse and large set of over 100 trillion possible MOFs using machine learning and evolutionary algorithm. It is demonstrated that our algorithm can discover 964 MOFs with methane working capacity over 200 cm 3 cm –3 and 96 MOFs with methane working capacity over the current world record of 208 cm 3 cm –3 . We believe that this methodology can take advantage of the modular nature of MOFs and can readily be extended to other important applications as well.
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- Authors
- Lee, S., Kim, B., Cho, H., Lee, H., Lee, S. Y., Cho, E. S., & Kim, J.
- Affiliations
- Korea Advanced Institute of Science and Technology (KR)
- Yonsei University (KR)
- Corresponding author
- Kim, Jihan
- Venue
- ACS Applied Materials & Interfaces
- Publisher
- American Chemical Society (ACS)
- Published
- 2021-05-14
- Volume
- 13
- Issue
- 20
- Pages
- 23647-23654
- ISSN
- 1944-8244 (ISSN-L)
- 1944-8252
- OpenAlex
- W3163360581
- Cited by
- 213
- References
- 30
- Citations by year
- 2026: 30 · 2025: 53 · 2024: 57 · 2023: 34 · 2022: 31 · 2021: 8
- FWCI
- 10.50
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- ACS Applied Materials & Interfaces (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Ministry of Science, ICT and Future Planning (2019M3F2A1072233, 2019M3F2A1072234)
- Topics
- Physical Sciences › Chemistry › Inorganic Chemistry › Metal-Organic Frameworks: Synthesis and Applications
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Materials Science › Materials Chemistry › Covalent Organic Framework Applications
- Keywords
- metal-organic frameworks, methane storage, computational materials screening, machine learning, evolutionary algorithms, work capacity, high-throughput screening
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