Capturing chemical intuition in synthesis of metal-organic frameworks.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 30710082.
- Also identified by DOI 10.1038/s41467-019-08483-9 and PMC identifier 6358622.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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.