Capturing chemical intuition in synthesis of metal-organic frameworks

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.

我们提出一种基于机器学习的方法论，旨在从金属有机框架合成过程中的一系列（部分）失败尝试中捕捉化学直觉。我们将化学直觉定义为合成化学家用于寻找合适合成条件的一系列不成文准则。由于（部分）失败实验通常未被报道，我们在成功探索能合成出迄今报道的最高比表面积HKUST-1的最优合成条件过程中，重构了失败实验的典型轨迹。我们阐明了量化这种化学直觉对新型材料合成的重要性。