Recycling#
As reported in Xu and Henkelman [RECYC_XH08], eOn implements a
method of saddle point recycling that can significantly reduce the computational
cost of the aKMC algorithm.
Suppose we are in reactant state \(R_0\), and we have discovered a series of saddles and their corresponding products, \(S_i\) and \(P_i\), respectively. Once we have reached confidence that we have found all energetically relevant processes, we select one of these processes and move to the corresponding product state.
For this example, let us assume that we have selected the process with saddle \(S_0\) and product \(P_0\). If we have found \(N\) energetically relevant processes in state \(R_0\), we can make suggestions of the saddle geometries \(G_i\) for saddles leading out of state \(P_0\), i.e.:
We use a min-mode following algorithm to converge these suggested saddle points. To reach confidence again in state \(P_0\), we need only perform saddle searches in the region around the atoms that moved significantly from state \(R_0\) to state \(P_0\), resulting in a significant reduction in computational costs. If this region is local, the overall cost does not increase with the total system size.
Configuration#
[Recycling]
References#
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.