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Title: SQERTSS: Dynamic rank based throttling of transition probabilities in kinetic Monte Carlo simulations

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 time-steps during KMC simulations, with the simulation spending an inordinate amount of KMC steps / cpu-time 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 speed-ranks 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 Quasi-Equilibrium Rank-based Throttling for Steady-state (SQERTSS) algorithm designed for use in achieving and simulating steady-state conditions in KMC simulations. Lastly, as shown in this work, the SQERTSS algorithm also works for transient conditions: themore » 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 time-steps may be distorted during the transient period.« less
Authors:
 [1] ;  [2] ;  [3] ; ORCiD logo [2]
  1. Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States). Dept. of Materials Science and Engineering
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Chemical Sciences Division
  3. 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:
Grant/Contract Number:
AC05-00OR22725
Type:
Accepted Manuscript
Journal Name:
Computer Physics Communications
Additional Journal Information:
Journal Volume: 219; Journal Issue: C; Journal ID: ISSN 0010-4655
Publisher:
Elsevier
Research Org:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org:
USDOE Laboratory Directed Research and Development (LDRD) Program
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; Non-equilibrium
OSTI Identifier:
1376303

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., 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. 2017. "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 time-steps during KMC simulations, with the simulation spending an inordinate amount of KMC steps / cpu-time 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 speed-ranks 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 Quasi-Equilibrium Rank-based Throttling for Steady-state (SQERTSS) algorithm designed for use in achieving and simulating steady-state 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 time-steps 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}
}