We introduce a new kinetic Monte Carlo (KMC) algorithm for off-lattice simulation. In off-lattice KMC, one needs to calculate the rates for all possible moves from the current state by searching the energy landscape for index 1 saddle points surrounding the current basin of attraction. We introduce a rejection scheme in which the true rates are replaced by rate estimates. This is done by first associating each saddle point with the atom that would move the most if that transition were to take place and then constructing an estimate for the total rate associated with each atom by using a nearest-neighbor bond count. These estimates allow one to select a set of possible transitions, one of which is accepted or rejected on the basis of a localized saddle point search focused on a particular atom. In principle, this allows a performance boost that scales with the number of particles in the system. We test the method on a growing two-species nanocluster and find we can reduce the computation time by 90% for clusters that contain ∼55 particles and 96% for clusters that contain ∼65 particles.

中文翻译：

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离格动力学蒙特卡洛模拟的排斥方案

我们介绍了一种新的动力学蒙特卡洛（KMC）算法，用于非晶格模拟。在非晶格KMC中，需要通过在能量景观中搜索当前吸引盆地周围的索引1鞍点来计算当前状态下所有可能移动的速率。我们引入了一种拒绝方案，在该方案中，将真实汇率替换为汇率估算。这是通过首先将每个鞍点与如果发生跃迁会移动最多的原子相关联，然后通过使用最近邻居键数来构造与每个原子相关的总速率的估计来完成的。这些估计值使人们可以选择一组可能的过渡，其中一个过渡是基于专注于特定原子的局部鞍点搜索而被接受或拒绝的。原则，这样可以提高性能，并随系统中的粒子数量扩展。我们在正在增长的两种物种的纳米簇上测试了该方法，发现对于包含约55个粒子的簇，可以将计算时间减少90％，对于包含约65个粒子的簇，可以将计算时间减少96％。