Capturing chemical intuition in synthesis of metal-organic frameworks

Seyed Mohamad Moosavi, Arunraj Chidambaram, Leopold Talirz, Maciej Haranczyk, Kyriakos C. Stylianou and Berend Smit ()
Additional contact information
Seyed Mohamad Moosavi: École Polytechnique Fédérale de Lausanne (EPFL)
Arunraj Chidambaram: École Polytechnique Fédérale de Lausanne (EPFL)
Leopold Talirz: École Polytechnique Fédérale de Lausanne (EPFL)
Maciej Haranczyk: IMDEA Materials Institute
Kyriakos C. Stylianou: École Polytechnique Fédérale de Lausanne (EPFL)
Berend Smit: École Polytechnique Fédérale de Lausanne (EPFL)

Nature Communications, 2019, vol. 10, issue 1, 1-7

Abstract: Abstract 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.

Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-019-08483-9 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-08483-9

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-019-08483-9

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().