Capturing chemical intuition in synthesis of metal-organic frameworks.
- Moosavi SM Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Valais, École Polytechnique Fédérale de Lausanne (EPFL), Rue de l'Industrie 17, CH-1951, Sion, Switzerland.
- Chidambaram A Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Valais, École Polytechnique Fédérale de Lausanne (EPFL), Rue de l'Industrie 17, CH-1951, Sion, Switzerland.
- Talirz L Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Valais, École Polytechnique Fédérale de Lausanne (EPFL), Rue de l'Industrie 17, CH-1951, Sion, Switzerland.
- Haranczyk M IMDEA Materials Institute, C/Eric Kandel 2, 28906, Getafe, Madrid, Spain.
- Stylianou KC Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Valais, École Polytechnique Fédérale de Lausanne (EPFL), Rue de l'Industrie 17, CH-1951, Sion, Switzerland.
- Smit B Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Valais, École Polytechnique Fédérale de Lausanne (EPFL), Rue de l'Industrie 17, CH-1951, Sion, Switzerland. berend.smit@epfl.ch.
- 2019-02-03
Published in:
- Nature communications. - 2019
General Biochemistry, Genetics and Molecular Biology
General Physics and Astronomy
General Chemistry
English
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.
- Language
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- English
- Open access status
- gold
- Identifiers
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- DOI 10.1038/s41467-019-08483-9
- PMID 30710082
- Persistent URL
- https://folia.unifr.ch/global/documents/16667
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