Paper Info
Title
The F-t-Pj-RG method: An adjacent-rolling-windows based steady-state detection technique for application to kinetic Monte Carlo simulations.
Abstract
A window-based steady-state detection algorithm has been developed for application to kinetic Monte Carlo simulation data. The algorithm, termed F-t-Pj-RG sequentially applies an F-test, a t-test, and a projection test on adjacent windows of the data while rolling (or shifting) and growing the windows when any of the tests fail. In aggregate, the algorithm is able to (a) automatically reject the warm-up period as not being at steady-state, as well as (b) determine an appropriate window size for converged statistics when sampling the data, which is necessary for detection of steady-state, and (c) detect steady-state within a particular tolerance. The last step, the projection test, is actually an oscillating-slope projection test, and is performed on j sequential data windows (i.e., more than two adjacent windows). It requires more than simply being within the user defined tolerance: the oscillating-slope projection test includes a condition that the slope must oscillate around zero when ≥2, which is an additional indication of steady-state. When all three tests are passed, the F-t-Pj test is passed, indicating that the prerequisites of steady-state detection have been met and also that conditions consistent with the definition of steady-state have been realized. This algorithm is applied to a variety of data sets that correspond to the diverse type of data trends that can be produced by kinetic Monte Carlo simulations. The algorithm is shown to be robust in its ability to handle differing functional forms, and is able to detect steady-state with low computational cost. The low computational cost of this method and its robustness towards varied data trends make it suitable for on-the-fly use in kinetic Monte Carlo simulations.
Year
DOI
Venue
2018
10.1016/j.cpc.2018.05.013
Computer Physics Communications
Keywords
Field
DocType
Steady-state detection,Kinetic Monte Carlo simulations,Complex chemical reactions
Sequential data,Oscillation,Data set,Mathematical analysis,Algorithm,Robustness (computer science),Kinetic Monte Carlo,Sampling (statistics),Steady state,Mathematics
Journal
Volume
ISSN
Citations 
232
0010-4655
0
PageRank 
References 
Authors
0.34
3
4
Name
Order
Citations
PageRank
Chris Nellis100.34
Thomas Danielson210.77
Aditya Savara310.77
Céline Hin410.77