Italian Institute of Technology, Via E. Melen 83, 16152, Genova, Italy.
J Phys Chem Lett. 2022 Feb 17;13(6):1424-1430. doi: 10.1021/acs.jpclett.1c03993. Epub 2022 Feb 4.
Over the last few decades, enhanced sampling methods have been continuously improved. Here, we exploit this progress and propose a modular workflow for blind reaction discovery and determination of reaction paths. In a three-step strategy, at first we use a collective variable derived from spectral graph theory in conjunction with the explore variant of the on-the-fly probability enhanced sampling method to drive reaction discovery runs. Once different chemical products are determined, we construct an ad-hoc neural network-based collective variable to improve sampling, and finally we refine the results using the free energy perturbation theory and a more accurate Hamiltonian. We apply this strategy to both intramolecular and intermolecular reactions. Our workflow requires minimal user input and extends the power of molecular dynamics to explore and characterize the reaction space.
在过去的几十年中，增强采样方法不断得到改进。在这里，我们利用这一进展，提出了一种用于盲反应发现和反应路径确定的模块化工作流程。在三步策略中，首先我们使用源自谱图理论的集体变量，结合即时概率增强采样方法的 explore 变体来驱动反应发现运行。一旦确定了不同的化学产物，我们就构建一个基于特定神经网络的集体变量来改进采样，最后我们使用自由能微扰理论和更精确的哈密顿量来细化结果。我们将此策略应用于分子内和分子间反应。我们的工作流程需要最小的用户输入，并扩展了分子动力学的功能，以探索和描述反应空间。
