The FtPjRG method: An adjacentrollingwindows based steadystate detection technique for application to kinetic Monte Carlo simulations
Abstract
A windowbased steadystate detection algorithm has been developed for application to kinetic Monte Carlo simulation data. The algorithm, termed FtPjRG sequentially applies an Ftest, a ttest, 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 warmup period as not being at steadystate, as well as (b) determine an appropriate window size for converged statistics when sampling the data, which is necessary for detection of steadystate, and (c) detect steadystate within a particular tolerance. The last step, the projection test, is actually an oscillatingslope 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 oscillatingslope projection test includes a condition that the slope must oscillate around zero when 2, which is an additional indication of steadystate. When all three tests are passed, the FtPj test is passed, indicating that the prerequisites of steadystate detection have been met and also that conditions consistent with the definition of steadystate have been realized. This algorithm is applied to a varietymore »
 Authors:

 Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States). Dept. of Mechanical Engineering
 Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States). Dept. of Materials Science and Engineering
 Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Chemical Sciences Division
 Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States). Dept. of Mechanical Engineering; Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States). Dept. of Materials Science and Engineering
 Publication Date:
 Research Org.:
 Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
 Sponsoring Org.:
 USDOE
 OSTI Identifier:
 1495967
 Alternate Identifier(s):
 OSTI ID: 1548150
 Grant/Contract Number:
 AC0500OR22725; LOIS 8457
 Resource Type:
 Accepted Manuscript
 Journal Name:
 Computer Physics Communications
 Additional Journal Information:
 Journal Volume: 232; Journal Issue: C; Journal ID: ISSN 00104655
 Publisher:
 Elsevier
 Country of Publication:
 United States
 Language:
 English
 Subject:
 97 MATHEMATICS AND COMPUTING; Steadystate detection; Kinetic Monte Carlo simulations; Complex chemical reactions
Citation Formats
Nellis, Chris, Danielson, Thomas, Savara, Aditya, and Hin, Celine. The FtPjRG method: An adjacentrollingwindows based steadystate detection technique for application to kinetic Monte Carlo simulations. United States: N. p., 2018.
Web. doi:10.1016/j.cpc.2018.05.013.
Nellis, Chris, Danielson, Thomas, Savara, Aditya, & Hin, Celine. The FtPjRG method: An adjacentrollingwindows based steadystate detection technique for application to kinetic Monte Carlo simulations. United States. doi:10.1016/j.cpc.2018.05.013.
Nellis, Chris, Danielson, Thomas, Savara, Aditya, and Hin, Celine. Sat .
"The FtPjRG method: An adjacentrollingwindows based steadystate detection technique for application to kinetic Monte Carlo simulations". United States. doi:10.1016/j.cpc.2018.05.013. https://www.osti.gov/servlets/purl/1495967.
@article{osti_1495967,
title = {The FtPjRG method: An adjacentrollingwindows based steadystate detection technique for application to kinetic Monte Carlo simulations},
author = {Nellis, Chris and Danielson, Thomas and Savara, Aditya and Hin, Celine},
abstractNote = {A windowbased steadystate detection algorithm has been developed for application to kinetic Monte Carlo simulation data. The algorithm, termed FtPjRG sequentially applies an Ftest, a ttest, 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 warmup period as not being at steadystate, as well as (b) determine an appropriate window size for converged statistics when sampling the data, which is necessary for detection of steadystate, and (c) detect steadystate within a particular tolerance. The last step, the projection test, is actually an oscillatingslope 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 oscillatingslope projection test includes a condition that the slope must oscillate around zero when 2, which is an additional indication of steadystate. When all three tests are passed, the FtPj test is passed, indicating that the prerequisites of steadystate detection have been met and also that conditions consistent with the definition of steadystate 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 steadystate with low computational cost. Finally, the low computational cost of this method and its robustness towards varied data trends make it suitable for onthefly use in kinetic Monte Carlo simulations.},
doi = {10.1016/j.cpc.2018.05.013},
journal = {Computer Physics Communications},
number = C,
volume = 232,
place = {United States},
year = {2018},
month = {6}
}
Web of Science
Figures / Tables:
Works referencing / citing this record:
A Practical Guide to Surface Kinetic Monte Carlo Simulations
journal, April 2019
 Andersen, Mie; Panosetti, Chiara; Reuter, Karsten
 Frontiers in Chemistry, Vol. 7
Figures / Tables found in this record: