Garay-Ruiz Diego, Álvarez-Moreno Moises, Bo Carles, Martínez-Núñez Emilio
Institute of Chemical Research of Catalonia (ICIQ), Barcelona Institute of Science & Technology (BIST), Avinguda Països Catalans, 16, 43007 Tarragona, Spain.
Departament de Química Física i Inorgànica, Universitat Rovira i Virgili (URV), Marcel·lí Domingo s/n, 43007 Tarragona, Spain.
ACS Phys Chem Au. 2022 Feb 4;2(3):225-236. doi: 10.1021/acsphyschemau.1c00051. eCollection 2022 May 25.
The level of detail attained in the computational description of reaction mechanisms can be vastly improved through tools for automated chemical space exploration, particularly for systems of small to medium size. Under this approach, the unimolecular decomposition landscape for indole was explored through the automated reaction mechanism discovery program AutoMeKin. Nevertheless, the sheer complexity of the obtained mechanisms might be a hindrance regarding their chemical interpretation. In this spirit, the new Python library has been designed to read and manipulate complex reaction networks, greatly simplifying their overall analysis. The package provides interactive dashboards featuring visualizations of the network, the three-dimensional (3D) molecular structures and vibrational normal modes of all chemical species, and the corresponding energy profiles for selected pathways. The combination of the joined mechanism generation and postprocessing workflow with the rich chemistry of indole decomposition enabled us to find new details of the reaction (obtained at the CCSD(T)/aug-cc-pVTZ//M06-2X/MG3S level of theory) that were not reported before: (i) 16 pathways leading to the formation of HCN and NH (via amino radical); (ii) a barrierless reaction between methylene radical and phenyl isocyanide, which might be an operative mechanism under the conditions of the interstellar medium; and (iii) reaction channels leading to both hydrogen cyanide and hydrogen isocyanide, of potential astrochemical interest as the computed HNC/HCN ratios greatly exceed the calculated equilibrium value at very low temperatures. The reported reaction networks can be very valuable to supplement databases of kinetic data, which is of remarkable interest for pyrolysis and astrochemical studies.
通过自动化学空间探索工具，尤其是对于中小规模的系统，可以极大地提高反应机理计算描述中的细节水平。在这种方法下，通过自动反应机理发现程序AutoMeKin探索了吲哚的单分子分解态势。然而，所获得机理的极度复杂性可能会妨碍对其进行化学解释。本着这种精神，设计了新的Python库来读取和处理复杂的反应网络，大大简化了对它们的整体分析。该软件包提供交互式仪表板，具有网络可视化、所有化学物种的三维（3D）分子结构和振动简正模式，以及选定路径的相应能量分布。联合机理生成和后处理工作流程与吲哚分解丰富化学性质的结合，使我们能够发现以前未报道的反应新细节（在CCSD(T)/aug-cc-pVTZ//M06-2X/MG3S理论水平上获得）：（i）16条导致形成HCN和NH（通过氨基自由基）的路径；（ii）亚甲基自由基与苯基异氰化物之间的无势垒反应，这可能是星际介质条件下的一种作用机制；（iii）导致氰化氢和异氰化氢的反应通道，由于计算出的HNC/HCN比值在非常低的温度下大大超过计算出的平衡值，因此具有潜在的天体化学意义。所报道的反应网络对于补充动力学数据库非常有价值，这对于热解和天体化学研究具有显著意义。
