Synthesis of Metal-Organic Frameworks: capturing chemical intuition

Authors: Seyed Mohamad Moosavi1, Arunraj Chidambaram1, Leopold Talirz1,2, Kyriakos C. Stylianou1, Berend Smit1,3*, Maciej Haranczyk4

  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. Department of Chemical and Biomolecular Engineering, University of California, Berkeley, California 94720, United States
  4. IMDEA Materials Institute, C/Eric Kandel 2, 28906 Getafe, Madrid, Spain
  • Corresponding author email: berend.smit@epfl.ch

(version: v1, submitted on: 14 July 2018)

How to cite this entry

DOI10.24435/materialscloud:2018.0011/v1

Seyed Mohamad Moosavi, Arunraj Chidambaram, Leopold Talirz, Kyriakos C. Stylianou, Berend Smit, Maciej Haranczyk, Synthesis of Metal-Organic Frameworks: capturing chemical intuition, Materials Cloud Archive (2018), doi: 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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Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.

External references

Software (Web application for finding optimal synthesis conditions)
Leopold Talirz, & SeyedMohamadMoosavi. (2018, July 16). ltalirz/sycofinder: First alpha release (Version v0.1.0a1). Zenodo. doi:10.5281/zenodo.1312815

Keywords

Machine learning Synthesis Optimisation Metal-Organic frameworks MARVEL

Version history

14 July 2018 [This version]