SQERTSS: Dynamic rank based throttling of transition probabilities in kinetic Monte Carlo simulations
Abstract
Lattice based Kinetic Monte Carlo (KMC) simulations offer a powerful simulation technique for investigating large reaction networks while retaining spatial configuration information, unlike ordinary differential equations. However, large chemical reaction networks can contain reaction processes with rates spanning multiple orders of magnitude. This can lead to the problem of “KMC stiffness” (similar to stiffness in differential equations), where the computational expense has the potential to be overwhelmed by very short timesteps during KMC simulations, with the simulation spending an inordinate amount of KMC steps / cputime simulating fast frivolous processes (FFPs) without progressing the system (reaction network). In order to achieve simulation times that are experimentally relevant or desired for predictions, a dynamic throttling algorithm involving separation of the processes into speedranks based on event frequencies has been designed and implemented with the intent of decreasing the probability of FFP events, and increasing the probability of slow process events  allowing rate limiting events to become more likely to be observed in KMC simulations. This Staggered QuasiEquilibrium Rankbased Throttling for Steadystate (SQERTSS) algorithm designed for use in achieving and simulating steadystate conditions in KMC simulations. Lastly, as shown in this work, the SQERTSS algorithm also works for transient conditions: themore »
 Authors:

 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 Materials Science and Engineering; Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States). Dept. of Mechanical Engineering
 Publication Date:
 Research Org.:
 Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
 Sponsoring Org.:
 USDOE Laboratory Directed Research and Development (LDRD) Program
 OSTI Identifier:
 1376303
 Alternate Identifier(s):
 OSTI ID: 1550330
 Grant/Contract Number:
 AC0500OR22725
 Resource Type:
 Accepted Manuscript
 Journal Name:
 Computer Physics Communications
 Additional Journal Information:
 Journal Volume: 219; Journal Issue: C; Journal ID: ISSN 00104655
 Publisher:
 Elsevier
 Country of Publication:
 United States
 Language:
 English
 Subject:
 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; 97 MATHEMATICS AND COMPUTING; Kinetic Monte Carlo; Stiffness; Steady state; Nonequilibrium
Citation Formats
Danielson, Thomas, Sutton, Jonathan E., Hin, Céline, and Savara, Aditya. SQERTSS: Dynamic rank based throttling of transition probabilities in kinetic Monte Carlo simulations. United States: N. p., 2017.
Web. doi:10.1016/j.cpc.2017.05.016.
Danielson, Thomas, Sutton, Jonathan E., Hin, Céline, & Savara, Aditya. SQERTSS: Dynamic rank based throttling of transition probabilities in kinetic Monte Carlo simulations. United States. doi:10.1016/j.cpc.2017.05.016.
Danielson, Thomas, Sutton, Jonathan E., Hin, Céline, and Savara, Aditya. Fri .
"SQERTSS: Dynamic rank based throttling of transition probabilities in kinetic Monte Carlo simulations". United States. doi:10.1016/j.cpc.2017.05.016. https://www.osti.gov/servlets/purl/1376303.
@article{osti_1376303,
title = {SQERTSS: Dynamic rank based throttling of transition probabilities in kinetic Monte Carlo simulations},
author = {Danielson, Thomas and Sutton, Jonathan E. and Hin, Céline and Savara, Aditya},
abstractNote = {Lattice based Kinetic Monte Carlo (KMC) simulations offer a powerful simulation technique for investigating large reaction networks while retaining spatial configuration information, unlike ordinary differential equations. However, large chemical reaction networks can contain reaction processes with rates spanning multiple orders of magnitude. This can lead to the problem of “KMC stiffness” (similar to stiffness in differential equations), where the computational expense has the potential to be overwhelmed by very short timesteps during KMC simulations, with the simulation spending an inordinate amount of KMC steps / cputime simulating fast frivolous processes (FFPs) without progressing the system (reaction network). In order to achieve simulation times that are experimentally relevant or desired for predictions, a dynamic throttling algorithm involving separation of the processes into speedranks based on event frequencies has been designed and implemented with the intent of decreasing the probability of FFP events, and increasing the probability of slow process events  allowing rate limiting events to become more likely to be observed in KMC simulations. This Staggered QuasiEquilibrium Rankbased Throttling for Steadystate (SQERTSS) algorithm designed for use in achieving and simulating steadystate conditions in KMC simulations. Lastly, as shown in this work, the SQERTSS algorithm also works for transient conditions: the correct configuration space and final state will still be achieved if the required assumptions are not violated, with the caveat that the sizes of the timesteps may be distorted during the transient period.},
doi = {10.1016/j.cpc.2017.05.016},
journal = {Computer Physics Communications},
number = C,
volume = 219,
place = {United States},
year = {2017},
month = {6}
}
Web of Science