Data-Driven Search Algorithm for Discovery of Synthesizable Zeolitic Imidazolate Frameworks

Zeolitic imidazolate frameworks (ZIFs), metal-organic analogues of zeolites, hold great potential for carbon-neutral applications due to their exceptional stability and porosity. However, ZIF discovery has been hindered by the limited topologies resulting from a mismatch between numerous predicted and few synthesized zeolitic networks. To address this, we propose a data-driven search algorithm using structural descriptors of known materials as a screening tool. From over 4 million zeolite structures, we identified potential ZIF candidates based on O-T-O angle differences, vertex symbols, and T-O-T angles. Energy calculations facilitated the ranking of ZIFs by their synthesizability, leading to the successful synthesis of three ZIFs with two novel topologies: UZIF-31 (<i>uft</i>1) and UZIF-32, -33 (<i>uft</i>2). Notably, UZIF-33 exhibited remarkable CO<sub>2</sub> selective adsorption. This study highlights the synergistic potential of combining structural predictions with chemical intuition to advance material discovery.

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Publication Details

Journal
JACS Au
Published
2025-03-07
DOI
https://doi.org/10.1021/jacsau.5c00077
Citations
3
Primary Topic
Zeolite Catalysis and Synthesis
Type
article
Field-Weighted Citation Impact
1.61

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article

Data-Driven Search Algorithm for Discovery of Synthesizable Zeolitic Imidazolate Frameworks

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3 citations
JACS Au
Zeolite Catalysis and Synthesis
1.61
article

Data-Driven Search Algorithm for Discovery of Synthesizable Zeolitic Imidazolate Frameworks

Soochan Lee, Wonyoung Choe, D. ChangMo Yang, Joohan Nam, Hyunchul Oh, Eunchan Cho, Jung-Hoon Lee, Hyein Jeong, Yeong-Jin Kim, Dong Yun Shin, Sungyeop Jung
article en
3 citations

Abstract

Zeolitic imidazolate frameworks (ZIFs), metal-organic analogues of zeolites, hold great potential for carbon-neutral applications due to their exceptional stability and porosity. However, ZIF discovery has been hindered by the limited topologies resulting from a mismatch between numerous predicted and few synthesized zeolitic networks. To address this, we propose a data-driven search algorithm using structural descriptors of known materials as a screening tool. From over 4 million zeolite structures, we identified potential ZIF candidates based on O-T-O angle differences, vertex symbols, and T-O-T angles. Energy calculations facilitated the ranking of ZIFs by their synthesizability, leading to the successful synthesis of three ZIFs with two novel topologies: UZIF-31 (<i>uft</i>1) and UZIF-32, -33 (<i>uft</i>2). Notably, UZIF-33 exhibited remarkable CO<sub>2</sub> selective adsorption. This study highlights the synergistic potential of combining structural predictions with chemical intuition to advance material discovery.

JACS AuVol. 5(3)
Korea University (KR), Beijing Computational Science Research Center (CN), Artificial Intelligence in Medicine (Canada) (CA), Nano Carbon (Poland) (PL), PRG S&Tech (South Korea), Ulsan National Institute of Science and Technology (KR), Korea Institute of Science and Technology (KR)
Ulsan National Institute of Science and Technology, Korea University, Korea Institute of Science and Technology, Korea Institute of Science and Technology Information, National Research Foundation of Korea
Openalex Percentile: Top 18%
Zeolite Catalysis and Synthesis
1.61
DOIOpen Access PDFOpenAlex
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