Ramos-Sánchez Pablo, Harvey Jeremy N, Gámez José A
Digital R&D, Covestro Deutschland AG, Leverkusen, Germany.
Department of Chemistry, KU Leuven, Leuven, Belgium.
J Comput Chem. 2023 Jan 5;44(1):27-42. doi: 10.1002/jcc.27011. Epub 2022 Oct 14.
Algorithms that automatically explore the chemical space have been limited to chemical systems with a low number of atoms due to expensive involved quantum calculations and the large amount of possible reaction pathways. The method described here presents a novel solution to the problem of chemical exploration by generating reaction networks with heuristics based on chemical theory. First, a second version of the reaction network is determined through molecular graph transformations acting upon functional groups of the reacting. Only transformations that break two chemical bonds and form two new ones are considered, leading to a significant performance enhancement compared to previously presented algorithm. Second, energy barriers for this reaction network are estimated through quantum chemical calculations by a growing string method, which can also identify non-octet species missed during the previous step and further define the reaction network. The proposed algorithm has been successfully applied to five different chemical reactions, in all cases identifying the most important reaction pathways.
由于涉及量子计算成本高昂且可能的反应路径数量众多，自动探索化学空间的算法一直局限于原子数量较少的化学系统。本文所述方法提出了一种新颖的解决方案，通过基于化学理论的启发式方法生成反应网络来解决化学探索问题。首先，通过对反应物官能团进行分子图变换来确定反应网络的第二个版本。仅考虑那些断裂两个化学键并形成两个新化学键的变换，与先前提出的算法相比，这显著提高了性能。其次，通过量子化学计算采用增长弦方法估计该反应网络的能垒，该方法还可以识别上一步中遗漏的非八隅体物种并进一步定义反应网络。所提出的算法已成功应用于五个不同的化学反应，在所有情况下都识别出了最重要的反应路径。
