Capturing chemical intuition in synthesis of metal-organic frameworks

Moosavi, Seyed Mohamad; Chidambaram, Arunraj; Talirz, Leopold; Haranczyk, Maciej; Stylianou, Kyriakos C.; Smit, Berend

doi:10.1038/s41467-019-08483-9

Title: Capturing chemical intuition in synthesis of metal-organic frameworks

Journal Article · 31 January 2019 · Nature Communications

DOI: https://doi.org/10.1038/s41467-019-08483-9 · OSTI ID:1559180

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École Polytechnique Fédérale de Lausanne (EPFL), Sion (Switzerland)
École Polytechnique Fédérale de Lausanne (EPFL), Sion, Lausanne (Switzerland)
IMDEA Materials Institute, Getafe, Madrid (Spain)

We report a methodology using machine learning to capture chemical intuition from a set of (partially) failed attempts to synthesize a metal-organic framework. We define chemical intuition as the collection of unwritten guidelines used by synthetic chemists to find the right synthesis conditions. As (partially) failed experiments usually remain unreported, we have reconstructed a typical track of failed experiments in a successful search for finding the optimal synthesis conditions that yields HKUST-1 with the highest surface area reported to date. We illustrate the importance of quantifying this chemical intuition for the synthesis of novel materials.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1559180

Journal Information:: Nature Communications, Journal Name: Nature Communications Journal Issue: 1 Vol. 10; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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Improving Tabletability of Excipients by Metal-Organic Framework-Based Cocrystallization: a Study of Mannitol and CaCl2 Cheng, Hao; Wei, Yuanfeng; Wang, Shiru Pharmaceutical Research, Vol. 37, Issue 7 https://doi.org/10.1007/s11095-020-02850-8	journal	June 2020
Linking synthesis and structure descriptors from a large collection of synthetic records of zeolite materials Muraoka, Koki; Sada, Yuki; Miyazaki, Daiki Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-12394-0	journal	October 2019
Synthetic organic chemistry driven by artificial intelligence de Almeida, A. Filipa; Moreira, Rui; Rodrigues, Tiago Nature Reviews Chemistry, Vol. 3, Issue 10 https://doi.org/10.1038/s41570-019-0124-0	journal	August 2019
Fast-developing machine learning support complex system research in environmental chemistry Duan, Qiannan; Lee, Jianchao New Journal of Chemistry, Vol. 44, Issue 4 https://doi.org/10.1039/c9nj05717j	journal	January 2020
Local structure order parameters and site fingerprints for quantification of coordination environment and crystal structure similarity Zimmermann, Nils E. R.; Jain, Anubhav RSC Advances, Vol. 10, Issue 10 https://doi.org/10.1039/c9ra07755c	journal	January 2020
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A unified machine-learning protocol for asymmetric catalysis as a proof of concept demonstration using asymmetric hydrogenation Singh, Sukriti; Pareek, Monika; Changotra, Avtar Proceedings of the National Academy of Sciences, Vol. 117, Issue 3 https://doi.org/10.1073/pnas.1916392117	journal	January 2020
Single crystal growth of water-soluble metal complexes with the help of the nano-crystallization method Alvarez, Ricardo; Nievergelt, Philipp P.; Slyshkina, Ekaterina Royal Society of Chemistry https://doi.org/10.5167/uzh-188672	text	January 2020

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