Department of Chemistry, Faculty of Science, Hokkaido University, Sapporo, Japan.
Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo, Japan; email:
Annu Rev Phys Chem. 2023 Apr 24;74:287-311. doi: 10.1146/annurev-physchem-102822-101025. Epub 2023 Jan 31.
Predicting the whole process of a chemical reaction while solving kinetic equations presents an opportunity to realize an on-the-fly kinetic simulation that directly discovers chemical reactions with their product yields. Such a simulation avoids the combinatorial explosion of reaction patterns to be examined by narrowing the search space based on the kinetic analysis of the reaction path network, and would open a new paradigm beyond the conventional two-step approach, which requires a reaction path network prior to performing a kinetic simulation. The authors addressed this issue and developed a practical method by combining the artificial force induced reaction method with the rate constant matrix contraction method. Two algorithms are available for this purpose: a forward mode with reactants as the input and a backward mode with products as the input. This article first numerically verifies these modes for known reactions and then demonstrates their application to the actual reaction discovery. Finally, the challenges of this method and the prospects for ab initio reaction discovery are discussed.
预测化学反应的全过程，同时求解动力学方程，为实现实时动力学模拟提供了机会，这种模拟可以直接发现具有产物产率的化学反应。这种模拟通过基于反应路径网络的动力学分析来缩小搜索空间，避免了通过检查反应模式的组合爆炸，从而超越了传统的两步法，这种两步法在进行动力学模拟之前需要一个反应路径网络。作者通过将人为力诱导反应法与速率常数矩阵收缩法相结合，解决了这一问题，并开发了一种实用的方法。为此目的，有两种算法可用：一种是将反应物作为输入的正向模式，另一种是将产物作为输入的反向模式。本文首先对已知反应进行了数值验证，然后展示了它们在实际反应发现中的应用。最后，讨论了该方法的挑战和从头发现反应的前景。
