Zhao Qiyuan, Savoie Brett M
Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN, USA.
Nat Comput Sci. 2021 Jul;1(7):479-490. doi: 10.1038/s43588-021-00101-3. Epub 2021 Jul 22.
Automated reaction prediction has the potential to elucidate complex reaction networks for applications ranging from combustion to materials degradation, but computational cost and inconsistent reaction coverage are still obstacles to exploring deep reaction networks. Here we show that cost can be reduced and reaction coverage can be increased simultaneously by relatively straightforward modifications of the reaction enumeration, geometry initialization and transition state convergence algorithms that are common to many prediction methodologies. These components are implemented in the context of yet another reaction program (YARP), our reaction prediction package with which we report reaction discovery benchmarks for organic single-step reactions, thermal degradation of a γ-ketohydroperoxide, and competing ring-closures in a large organic molecule. Compared with recent benchmarks, YARP (re)discovers both established and unreported reaction pathways and products while simultaneously reducing the cost of reaction characterization by nearly 100-fold and increasing convergence of transition states. This combination of ultra-low cost and high reaction coverage creates opportunities to explore the reactivity of larger systems and more complex reaction networks for applications such as chemical degradation, where computational cost is a bottleneck.
自动反应预测有潜力阐明从燃烧到材料降解等各种应用中的复杂反应网络，但计算成本和不一致的反应覆盖范围仍是探索深度反应网络的障碍。在此我们表明，通过对许多预测方法中常见的反应枚举、几何初始化和过渡态收敛算法进行相对简单的修改，可同时降低成本并增加反应覆盖范围。这些组件是在另一个反应程序（YARP）的框架内实现的，YARP是我们的反应预测软件包，我们用它报告了有机单步反应、γ-酮氢过氧化物的热降解以及大型有机分子中的竞争闭环反应的反应发现基准。与最近的基准相比，YARP重新发现了已有的和未报道的反应途径及产物，同时将反应表征成本降低了近100倍，并提高了过渡态的收敛性。这种超低计算成本和高反应覆盖范围的结合为探索更大体系的反应活性和更复杂的反应网络创造了机会，这些反应网络可应用于计算成本是瓶颈的化学降解等领域。
