Kaiser Waldemar, Gößwein Manuel, Gagliardi Alessio
Department of Electrical and Computer Engineering, Technical University of Munich, Arcisstrasse 21, 80333 Munich, Germany.
J Chem Phys. 2020 May 7;152(17):174106. doi: 10.1063/5.0002289.
Kinetic Monte Carlo (kMC) simulations are frequently used to study (electro-)chemical processes within science and engineering. kMC methods provide insight into the interplay of stochastic processes and can link atomistic material properties with macroscopic characteristics. Significant problems concerning the computational demand arise if processes with large time disparities are competing. Acceleration algorithms are required to make slow processes accessible. Especially, the accelerated superbasin kMC (AS-kMC) scheme has been frequently applied within chemical reaction networks. For larger systems, the computational overhead of the AS-kMC is significant as the computation of the superbasins is done during runtime and comes with the need for large databases. Here, we propose a novel acceleration scheme for diffusion and transport processes within kMC simulations. Critical superbasins are detected during the system initialization. Scaling factors for the critical rates within the superbasins, as well as a lower bound for the number of sightings, are derived. Our algorithm exceeds the AS-kMC in the required simulation time, which we demonstrate with a 1D-chain example. In addition, we apply the acceleration scheme to study the time-of-flight (TOF) of charge carriers within organic semiconductors. In this material class, time disparities arise due to a significant spread of transition rates. The acceleration scheme allows a significant acceleration up to a factor of 65 while keeping the error of the TOF values negligible. The computational overhead is negligible, as all superbasins only need to be computed once.
动力学蒙特卡罗（kMC）模拟常用于研究科学与工程领域中的（电）化学过程。kMC方法能深入了解随机过程之间的相互作用，并可将原子尺度的材料特性与宏观特征联系起来。如果存在时间尺度差异很大的过程相互竞争，就会出现计算需求方面的重大问题。需要加速算法来处理缓慢的过程。特别是，加速超级盆地kMC（AS-kMC）方案在化学反应网络中经常被应用。对于更大的系统，AS-kMC的计算开销很大，因为超级盆地的计算是在运行时进行的，并且需要大量的数据库。在此，我们提出了一种用于kMC模拟中扩散和输运过程的新型加速方案。在系统初始化期间检测关键超级盆地。推导了超级盆地内关键速率的缩放因子以及观测次数的下限。我们通过一个一维链示例证明，我们的算法在所需模拟时间方面优于AS-kMC。此外，我们应用该加速方案来研究有机半导体中电荷载流子的飞行时间（TOF）。在这类材料中，由于跃迁速率的显著差异会出现时间尺度差异。该加速方案能够实现高达65倍的显著加速，同时使TOF值的误差可忽略不计。计算开销可忽略不计，因为所有超级盆地只需计算一次。
