<?xml version='1.0' encoding='utf-8'?> <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"> <dc:creator>Moosavi, Seyed Mohamad</dc:creator> <dc:creator>Chidambaram, Arunraj</dc:creator> <dc:creator>Talirz, Leopold</dc:creator> <dc:creator>Haranczyk, Maciej</dc:creator> <dc:creator>Stylianou, Kyriakos C.</dc:creator> <dc:creator>Smit, Berend</dc:creator> <dc:date>2019-03-03</dc:date> <dc:description>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.</dc:description> <dc:identifier>https://archive.materialscloud.org/record/2018.0011/v4</dc:identifier> <dc:identifier>doi:10.24435/materialscloud:2018.0011/v4</dc:identifier> <dc:identifier>mcid:2018.0011/v4</dc:identifier> <dc:identifier>oai:materialscloud.org:50</dc:identifier> <dc:language>en</dc:language> <dc:publisher>Materials Cloud</dc:publisher> <dc:relation>https://www.materialscloud.org/work/tools/sycofinder</dc:relation> <dc:rights>info:eu-repo/semantics/openAccess</dc:rights> <dc:rights>Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights> <dc:subject>Machine learning</dc:subject> <dc:subject>Synthesis</dc:subject> <dc:subject>Optimisation</dc:subject> <dc:subject>Genetic algorithms</dc:subject> <dc:subject>Metal-Organic frameworks</dc:subject> <dc:subject>Robotic synthesi</dc:subject> <dc:subject>MARVEL</dc:subject> <dc:title>Capturing chemical intuition in synthesis of metal-organic frameworks</dc:title> <dc:type>Dataset</dc:type> </oai_dc:dc>