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Capturing chemical intuition in synthesis of metal-organic frameworks

Seyed Mohamad Moosavi1, Arunraj Chidambaram1, Leopold Talirz1,2, Maciej Haranczyk3, Kyriakos C. Stylianou1, Berend Smit1,4*

1 Laboratory of Molecular Simulation, Institut des Sciences et Ingénierie Chimiques, Valais, Ecole Polytechnique Fédérale de Lausanne (EPFL), Rue de l’Industrie 17, CH-1951 Sion, Switzerland

2 Theory and simulation of materials (THEOS), Faculté des Sciences et Techniques de l’Ingénieur, École Polytechnique Fédérale de Lausanne (EPFL), Station 9, CH-1015 Lausanne, Switzerland

3 IMDEA Materials Institute, C/Eric Kandel 2, 28906 Getafe, Madrid, Spain

4 Department of Chemical and Biomolecular Engineering, University of California, Berkeley, California 94720, United States

* Corresponding authors emails:
DOI10.24435/materialscloud:2018.0011/v3 [version v3]

Publication date: Jan 04, 2019

How to cite this record

Seyed Mohamad Moosavi, Arunraj Chidambaram, Leopold Talirz, Maciej Haranczyk, Kyriakos C. Stylianou, Berend Smit, Capturing chemical intuition in synthesis of metal-organic frameworks, Materials Cloud Archive 2018.0011/v3 (2019), doi: 10.24435/materialscloud:2018.0011/v3.


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.

Materials Cloud sections using this data


File name Size Description
14.5 MiB Video introducing the SyCoFinder web application which combines scripts used in this study to find optimal synthesis conditions.
3.9 KiB Synthesis conditions and their corresponding fitness scores for the synthesis trials of the three generations of the genetic algorithm optimization for Cu-HKUST-1


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External references

Journal reference (Article where the data is discussed)
Seyed Mohamad Moosavi, Arunraj Chidambaram, Leopold Talirz, Maciej Haranczyk, Kyriakos C. Stylianou, Berend Smit. Submitted.
Software (Web application for finding optimal synthesis conditions)
Leopold Talirz, & SeyedMohamadMoosavi. (2019, January 4). ltalirz/sycofinder: Release for (Version v0.1.0). Zenodo. doi:10.5281/zenodo.2531564


Machine learning Synthesis Optimisation Genetic algorithms Metal-Organic frameworks Robotic synthesi MARVEL