Department of Chemistry and Biochemistry and the Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712-0165, USA.
J Chem Phys. 2012 Jul 7;137(1):014105. doi: 10.1063/1.4730746.
Kinetic Monte Carlo is a method used to model the state-to-state kinetics of atomic systems when all reaction mechanisms and rates are known a priori. Adaptive versions of this algorithm use saddle searches from each visited state so that unexpected and complex reaction mechanisms can also be included. Here, we describe how calculated reaction mechanisms can be stored concisely in a kinetic database and subsequently reused to reduce the computational cost of such simulations. As all accessible reaction mechanisms available in a system are contained in the database, the cost of the adaptive algorithm is reduced towards that of standard kinetic Monte Carlo.
动力学蒙特卡罗方法是一种用于模拟原子系统状态到状态动力学的方法，前提是所有反应机制和速率都是已知的。这种算法的自适应版本从每个访问的状态进行鞍点搜索，以便也可以包括意外和复杂的反应机制。在这里，我们描述了如何在动力学数据库中简洁地存储计算得到的反应机制，并随后重新使用它们来降低这种模拟的计算成本。由于系统中所有可用的可及反应机制都包含在数据库中，因此自适应算法的成本降低到标准动力学蒙特卡罗的成本。
