Synthesis of Metal-Organic Frameworks: capturing chemical intuition

DOI

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.

Identifier
DOI https://doi.org/10.24435/materialscloud:2018.0011/v1
Source https://archive.materialscloud.org/record/2018.0011/v1
Metadata Access https://archive.materialscloud.org/xml?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai:materialscloud.org:47
Provenance
Creator Moosavi, Seyed Mohamad; Chidambaram, Arunraj; Talirz, Leopold; Haranczyk, Maciej; Stylianou, Kyriakos C.; Smit, Berend
Publisher Materials Cloud
Publication Year 2018
Rights info:eu-repo/semantics/openAccess; Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode
OpenAccess true
Contact archive(at)materialscloud.org
Representation
Language English
Resource Type Dataset
Discipline Materials Science and Engineering