10.24435/MATERIALSCLOUD:2018.0011/V2
Moosavi, Seyed Mohamad
Chidambaram, Arunraj
Talirz, Leopold
Haranczyk, Maciej
Stylianou, Kyriakos C.
Smit, Berend
Capturing chemical intuition in synthesis of metal-organic frameworks
Materials Cloud
2018
Machine learning
Synthesis
Optimisation
Genetic algorithms
Metal-Organic frameworks
Robotic synthesi
MARVEL
2018-12-10
en
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode
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.