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Capturing chemical intuition in synthesis of metal-organic frameworks

Author

Listed:
  • Seyed Mohamad Moosavi

    (École Polytechnique Fédérale de Lausanne (EPFL))

  • Arunraj Chidambaram

    (École Polytechnique Fédérale de Lausanne (EPFL))

  • Leopold Talirz

    (École Polytechnique Fédérale de Lausanne (EPFL)
    École Polytechnique Fédérale de Lausanne (EPFL))

  • Maciej Haranczyk

    (IMDEA Materials Institute)

  • Kyriakos C. Stylianou

    (École Polytechnique Fédérale de Lausanne (EPFL))

  • Berend Smit

    (École Polytechnique Fédérale de Lausanne (EPFL))

Abstract

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.

Suggested Citation

  • Seyed Mohamad Moosavi & Arunraj Chidambaram & Leopold Talirz & Maciej Haranczyk & Kyriakos C. Stylianou & Berend Smit, 2019. "Capturing chemical intuition in synthesis of metal-organic frameworks," Nature Communications, Nature, vol. 10(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-08483-9
    DOI: 10.1038/s41467-019-08483-9
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