Publication date: Jan 04, 2019
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.
|14.5 MiB||Video introducing the SyCoFinder web application which combines scripts used in this study to find optimal synthesis conditions.|
|3.9 KiB||Synthesis conditions and their corresponding fitness scores for the synthesis trials of the three generations of the genetic algorithm optimization for Cu-HKUST-1|
|2018.0011/v4 (version v4)||Mar 03, 2019||DOI10.24435/materialscloud:2018.0011/v4|
|2018.0011/v3 (version v3) [This version]||Jan 04, 2019||DOI10.24435/materialscloud:2018.0011/v3|
|2018.0011/v2 (version v2)||Dec 10, 2018||DOI10.24435/materialscloud:2018.0011/v2|
|2018.0011/v1 (version v1)||Jul 14, 2018||DOI10.24435/materialscloud:2018.0011/v1|