Adaptive Kinetic Monte Carlo#
The adaptive kinetic Monte Carlo (aKMC) method is a method to coarse grain molecular dynamics for rare event systems as described in Henkelman and Jónsson [AK_HJonsson01], Xu and Henkelman [AK_XH08]. A rare event system is one in which the interesting dynamics is governed by short transitions between stable states. The fast vibrational motion within a stable state is considered to be in equilibrium and described statisitcally. A transition between states is assumed to be first order, since it is a rare events, and the rate of the transition is calculated from the harmonic approximation to transition state theory (hTST).
The hTST approximation of a transition rate is calculated from the energy difference between the saddle point along the minimum energy path for the transition and the initial minimum. The vibrational modes at these points are also used to calculate the prefactor. An hTST rate is of the standard Arrhenius form \(R = v \exp (-\Delta E/kT)\) where \(v\) is the product of all positive modes at the minimum divided by those at the saddle, \(\Delta E\) is the energy barrier, and \(kT\) is the thermal enregy.
In order to propogate the dynamics within aKMC, a list of all possible rates leading away from the current stable state to any other state is required. Formally, there are an enourmously large number of such transitions (also called processes) available in a typical atomic system, but in fact only the transitions with the fastest rates with the highest probability of happening are required. The search for processes is then limited to those with rates on the same order as the fastest processes found.
The search for possible processes is the primary task of the aKMC simulations. Each client does a minimum mode (minmode) following search from the miminum of the current state and tries to find a saddle point which connects from the minimum in the current state to an adjacent state [AK_HJonsson99]. A saddle point is connected to a state if a minimization initiated along the negative mode at the saddle converges to the minimum of that state.
Each client is tasked with one or more such searches. It climbs from the minimum to a saddle, and if successful, it minimizes on either side of the saddle to determine the connecting states. The prefactor for the transition is also calculated by finite difference and the hTST rate is calculated. These data are reported back to the server.
Essentially, then, unlike traditional Kinetic Monte Carlo (KMC) methods, adaptive (a), or off-lattice KMC [AK_TMBelandH20] doesn’t require a lattice of predetermined points in configuration space. The method dynamically builds a rate table based on local atomic movements and energy barriers. This allows for more accurate modeling of complex systems where atomic positions are not fixed to a grid. The confidence scheme and thermal accessibility settings in AKMC ensure that transitions between states are based on well-defined criteria, enhancing the precision of simulations.
The server is reponsible for the time evolution of the system by the KMC algorithm. Each process leading to a new state is tabulated in a rate table and one transition is selected stochastically with a probability proportional to its rate. The transition time is drawn from a first-order distribution for the total rate of escape from the state.
A complete tutorial is also provided.
Configuration#
[akmc]
References#
Graeme Henkelman and Hannes Jónsson. A dimer method for finding saddle points on high dimensional potential surfaces using only first derivatives. The Journal of Chemical Physics, 111(15):7010–7022, October 1999. doi:10.1063/1.480097.
Graeme Henkelman and Hannes Jónsson. Long time scale kinetic Monte Carlo simulations without lattice approximation and predefined event table. The Journal of Chemical Physics, 115(21):9657–9666, November 2001. doi:10.1063/1.1415500.
Mickaël Trochet, Normand Mousseau, Laurent Karim Béland, and Graeme Henkelman. Off-Lattice Kinetic Monte Carlo Methods. In Wanda Andreoni and Sidney Yip, editors, Handbook of Materials Modeling: Methods: Theory and Modeling, pages 715–743. Springer International Publishing, Cham, 2020. doi:10.1007/978-3-319-44677-6_29.
Lijun Xu and Graeme Henkelman. Adaptive kinetic Monte Carlo for first-principles accelerated dynamics. The Journal of Chemical Physics, 129(11):114104, September 2008. doi:10.1063/1.2976010.