期刊
NATURE COMMUNICATIONS
卷 10, 期 -, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41467-019-08483-9
关键词
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资金
- NCCR MARVEL - Swiss National Science Foundation
- Deutsche Forschungsgemeinschaft (DFG) [SPP 1570]
- Swiss National Science Foundation (SNSF) [PZENP2_166888]
- Spanish Ministry of Economy and Competitiveness [RYC-2013-13949]
- European Research Council (ERC) under the European Union [666983]
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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