Seyed Mohamad Moosavi¹, Arunraj Chidambaram¹, Leopold Talirz^1,2, Maciej Haranczyk³, Kyriakos C. Stylianou¹, Berend Smit^1,4*

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: berend.smit@epfl.ch

DOI10.24435/materialscloud:2018.0011/v1 [version v1]

Publication date: Jul 14, 2018

How to cite this record

Seyed Mohamad Moosavi, Arunraj Chidambaram, Leopold Talirz, Maciej Haranczyk, Kyriakos C. Stylianou, Berend Smit, Synthesis of Metal-Organic Frameworks: capturing chemical intuition, Materials Cloud Archive 2018.0011/v1 (2018), https://doi.org/10.24435/materialscloud:2018.0011/v1

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.

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Keywords

Machine learning Synthesis Optimisation Metal-Organic frameworks MARVEL