Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland.
J Chem Inf Model. 2023 Jun 12;63(11):3392-3403. doi: 10.1021/acs.jcim.3c00102. Epub 2023 May 22.
Autonomously exploring chemical reaction networks with first-principles methods can generate vast data. Especially autonomous explorations without tight constraints risk getting trapped in regions of reaction networks that are not of interest. In many cases, these regions of the networks are only exited once fully searched. Consequently, the required human time for analysis and computer time for data generation can make these investigations unfeasible. Here, we show how simple reaction templates can facilitate the transfer of chemical knowledge from expert input or existing data into new explorations. This process significantly accelerates reaction network explorations and improves cost-effectiveness. We discuss the definition of the reaction templates and their generation based on molecular graphs. The resulting simple filtering mechanism for autonomous reaction network investigations is exemplified with a polymerization reaction.
自主运用第一性原理方法探索化学反应网络可以产生大量数据。特别是在没有严格约束的自主探索中，可能会陷入不感兴趣的反应网络区域。在许多情况下，这些网络区域只有在完全搜索后才能退出。因此，分析所需的人力时间和生成数据所需的计算机时间可能会使这些研究变得不可行。在这里，我们展示了简单的反应模板如何促进从专家输入或现有数据向新探索转移化学知识。这个过程显著加快了反应网络的探索速度，提高了成本效益。我们讨论了反应模板的定义及其基于分子图的生成。一个聚合反应说明了自主反应网络研究中生成的简单过滤机制。
