Metal‐Organic Frameworks with Rod Yttrium Secondary Building Units

Zheng, Zhiling; Rong, Zichao; Chen, Oscar Iu‐Fan; Yaghi, Omar M.

doi:10.1002/ijch.202300017

Cited by 4 publications

(2 citation statements)

References 36 publications

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“…We then created a dictionary linking each unique PubChem Compound identification number (CID) or Chemical Abstracts Service (CAS) number to multiple full names and abbreviations and generated the corresponding SMILES code. We note that for complicated linkers or those with missing full names, inappropriate nomenclature, or nonexistent CID or CAS numbers, − manual intervention was occasionally necessary to generate SMILES codes for such chemicals (Supporting Information, Figures S50–S54). However, most straightforward cases were handled efficiently by ChatGPT’s generated Python code.…”

Section: Methodsmentioning

confidence: 99%

ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis

et al. 2023

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We use prompt engineering to guide ChatGPT in the automation of text mining of metal–organic framework (MOF) synthesis conditions from diverse formats and styles of the scientific literature. This effectively mitigates ChatGPT’s tendency to hallucinate information, an issue that previously made the use of large language models (LLMs) in scientific fields challenging. Our approach involves the development of a workflow implementing three different processes for text mining, programmed by ChatGPT itself. All of them enable parsing, searching, filtering, classification, summarization, and data unification with different trade-offs among labor, speed, and accuracy. We deploy this system to extract 26 257 distinct synthesis parameters pertaining to approximately 800 MOFs sourced from peer-reviewed research articles. This process incorporates our ChemPrompt Engineering strategy to instruct ChatGPT in text mining, resulting in impressive precision, recall, and F1 scores of 90–99%. Furthermore, with the data set built by text mining, we constructed a machine-learning model with over 87% accuracy in predicting MOF experimental crystallization outcomes and preliminarily identifying important factors in MOF crystallization. We also developed a reliable data-grounded MOF chatbot to answer questions about chemical reactions and synthesis procedures. Given that the process of using ChatGPT reliably mines and tabulates diverse MOF synthesis information in a unified format while using only narrative language requiring no coding expertise, we anticipate that our ChatGPT Chemistry Assistant will be very useful across various other chemistry subdisciplines.

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Section: Methodsmentioning

confidence: 99%

ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis

et al. 2023

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“…We note that in all previously reported hydroxamate-MOMs with discrete PBUs where Ti/Zr/Hf/In/Fe cations were interconnected by benzene-1,4-dihydroxamate (BDHA) or E -ethylene-1,2-dihydroxamate, the two hydroxamate groups were in a trans - conformation, similar to Mode III in Scheme . Only in two examples where hydroxamate-MOFs with one-dimensional (1D) infinite secondary building units (SBUs) were reported, , cis -BDHA moieties were observed in the crystal structures. In summary, the incorporation of the “bent” TDHA ligand and additional coordination modes should provide a unique opportunity to diversify the structures of hydroxamate-based MOMs.…”

Section: Introductionmentioning

confidence: 99%

Structural Diversity in Ga/In-Hydroxamate Metal–Organic Materials

Wu,

Qin,

Duan

et al. 2024

Inorg. Chem.

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Developing metal–organic materials (MOMs) with chemical robustness is a prerequisite to exploring their intriguing properties and applications. As part of a continuing effort to construct robust MOMs featuring chelated building units, here we introduce a “bent” thiophene-2,5-dihydroxamate ligand with multiple intrinsic conformations when it is used as a chelating linkage. This approach should further diversify the coordination chemistry in hydroxamate-based MOM structures without compromising the stability. In combination with Group 13 metals Ga/In to ensure homoleptic metal vertices, we report the successful crystallization of four MOMs with diverse structures and dimensionalities: SUM-81 as a 0D metal–organic polyhedron (MOP), SUM-82 as a 2D MOF with an fes topology, SUM-83 and SUM-84 as distinct 1D coordination polymers with shapes mimic stairs and mesh tubes, respectively. As these structures indeed contain the aforementioned different ligand conformations and combinations thereof, these results expand our understanding of the coordination chemistry of hydroxamates. To demonstrate the potential applicability of hydroxamate-chelated robust MOMs, the permanently porous SUM-81 MOP was successfully incorporated in a series of mixed matrix membranes for CO2/N2 separation, showing impressive performances.

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