The researchers used machine learning to capture chemical intuition -- which they defined as the collection of unwritten guidelines chemists use to find the right synthesis conditions -- from a set of (partially) failed attempts to synthesize a metal-organic framework. Since these experiments are usually unreported, they reconstructed a typical track of failed experiments in the successful search for the optimal synthesis conditions for yielding a certain MOF with the highest surface area reported to date. They go on to show how important quantifying this chemical intuition is in the synthesis of novel materials.
Please follow this link to read EPFL journalist Sarah Perrin's story on the research.
"Capturing chemical intuition in synthesis of metalorganic frameworks", Seyed Mohamad Moosavi, Arunraj Chidambaram, Leopold Talirz, Maciej Haranczyk, Kyriakos C. Stylianou, Berend Smit. Published in Nature Communications on February 1st, 2019. DOI: 10.1038/s41467-019-08483-9.
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