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Intuition and failure as valuable ingredients in chemical research

Methodology to collect lessons learned from partially failed trials and incorrect hypotheses

07-Feb-2019

Free-photo; pixabay.com; CC0

Seyed Mohamad Moosavi, Arunraj Chidambaram, Leopold Talirz, Maciej Haranczyk, Kyriakos C. Stylianou & Berend Smit

Graphic showing how the three components of the framework - synthesis, optimization, and machine learning - interact.

Researchers from the lab of NCCR MARVEL's deputy director Berend Smit and colleagues have developed a methodology for collecting the lessons learned from partially failed trials and incorrect hypotheses -- the experiments that didn't work.

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.

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