Computational Screening of Trillions of Metal-Organic Frameworks for High-Performance Methane Storage

Sangwon Lee, Baekjun Kim, Hyun Cho, Hooseung Lee, Sarah Yunmi Lee, Eun Seon Cho, Jihan Kim

Research output: Contribution to journalArticlepeer-review

41 Citations (Scopus)


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 cm3 cm-3 and 96 MOFs with methane working capacity over the current world record of 208 cm3 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.

Original languageEnglish
Pages (from-to)23647-23654
Number of pages8
JournalACS Applied Materials and Interfaces
Issue number20
Publication statusPublished - 2021 May 26

Bibliographical note

Funding Information:
This research was supported by Energy Cloud R&D Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (2019M3F2A1072233 and 2019M3F2A1072234).

Publisher Copyright:
© 2021 American Chemical Society.

All Science Journal Classification (ASJC) codes

  • Materials Science(all)


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