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Title: The F-t-Pj-RG method: An adjacent-rolling-windows based steady-state detection technique for application to kinetic Monte Carlo simulations

Journal Article · · Computer Physics Communications
 [1];  [2]; ORCiD logo [3];  [4]
  1. Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States). Dept. of Mechanical Engineering
  2. Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States). Dept. of Materials Science and Engineering
  3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Chemical Sciences Division
  4. 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
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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. Finally, 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.

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC05-00OR22725; LOIS 8457
OSTI ID:
1495967
Alternate ID(s):
OSTI ID: 1548150
Journal Information:
Computer Physics Communications, Vol. 232, Issue C; ISSN 0010-4655
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 4 works
Citation information provided by
Web of Science

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Cited By (2)

A Practical Guide to Surface Kinetic Monte Carlo Simulations journal April 2019
A Practical Guide to Surface Kinetic Monte Carlo Simulations text January 2019

Figures / Tables (20)

Figure 1(p. 5)figure Figure 1
Figure 2(p. 17)figure Figure 2
Figure 3(p. 19)figure Figure 3
Table 1(p. 20)table Table 1
Figure 4(p. 21)figure Figure 4
Table 2(p. 21)table Table 2
Figure 5(p. 22)figure Figure 5
Table 3(p. 22)table Table 3
Figure 6(p. 23)figure Figure 6
Table 4(p. 23)table Table 4
Figure 7(p. 24)figure Figure 7
Table 5(p. 24)table Table 5
Figure 8(p. 25)figure Figure 8
Table 6(p. 26)table Table 6
Figure 9(p. 27)figure Figure 9
Figure 10(p. 28)figure Figure 10
Table 7(p. 28)table Table 7
Figure A.1(p. 35)figure Figure A.1
Figure A.2(p. 35)figure Figure A.2
Figure A.3(p. 42)figure Figure A.3

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97 MATHEMATICS AND COMPUTING
Steady-state detection
Kinetic Monte Carlo simulations
Complex chemical reactions