Capturing Chemical Intuition in Synthesis of Metal-organic Frameworks

Overview

Journal Nat Commun

Specialty Biology

Date 2019 Feb 3

PMID 30710082

Citations 40

Authors

Seyed Mohamad Moosavi

Arunraj Chidambaram

Leopold Talirz

Maciej Haranczyk

Kyriakos C Stylianou

Berend Smit

Affiliations

Soon will be listed here.

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.

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