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/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), https://doi.org/10.24435/materialscloud:2018.0011/v3

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.

Materials Cloud sections using this data

Files

File name	Size	Description
SyCoFinder_movie.mp4 MD5md5:8fa477622aeb32893d1c7440ff9ce009	14.5 MiB	Video introducing the SyCoFinder web application which combines scripts used in this study to find optimal synthesis conditions.
CuBTC_synthesis.csv MD5md5:ee3a5f860cc84bb1170bad02aa995080	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

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

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