General Method for Speeding Up Kinetic Monte Carlo Simulations

Kinetic Monte Carlo (MC) is the main stochastic strategy used to simulate polymerization systems, as it gives good results with simple formulation. Normally, the algorithm used in this method presents high computational times, being necessary to choose suitable control volume sizes, which gives reli...

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Published inIndustrial & engineering chemistry research Vol. 59; no. 19; pp. 9034 - 9042
Main Authors Rego, Artur S. C, Brandão, Amanda L. T
Format Journal Article
LanguageEnglish
Published American Chemical Society 13.05.2020
Subjects
algorithms
polymerization
process design
Python
Online AccessGet full text
ISSN0888-5885
1520-5045
1520-5045
DOI10.1021/acs.iecr.0c01069

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Abstract Kinetic Monte Carlo (MC) is the main stochastic strategy used to simulate polymerization systems, as it gives good results with simple formulation. Normally, the algorithm used in this method presents high computational times, being necessary to choose suitable control volume sizes, which gives reliable results in moderate simulation times. The use of high-level languages (Python, MATLAB) over low-level languages (C, Fortran) usually aggravates this scenario, as it is slower despite being easier to use. The current study presents a simple method for speeding up the MC simulation of polymerization reactions. First, the code itself is optimized to reduce by half the computational time required compared with the original code, and then a benchmark of pure Python and Python with Numba is made. The results show a drop in the computational times above 99% when using Numba instead of pure Python codes.
AbstractList Kinetic Monte Carlo (MC) is the main stochastic strategy used to simulate polymerization systems, as it gives good results with simple formulation. Normally, the algorithm used in this method presents high computational times, being necessary to choose suitable control volume sizes, which gives reliable results in moderate simulation times. The use of high-level languages (Python, MATLAB) over low-level languages (C, Fortran) usually aggravates this scenario, as it is slower despite being easier to use. The current study presents a simple method for speeding up the MC simulation of polymerization reactions. First, the code itself is optimized to reduce by half the computational time required compared with the original code, and then a benchmark of pure Python and Python with Numba is made. The results show a drop in the computational times above 99% when using Numba instead of pure Python codes.
Author Rego, Artur S. C
Brandão, Amanda L. T
AuthorAffiliation Department of Chemical and Materials Engineering (DEQM)
Pontifı́cia Universidade Católica do Rio de Janeiro
AuthorAffiliation_xml – name: Pontifı́cia Universidade Católica do Rio de Janeiro
– name: Department of Chemical and Materials Engineering (DEQM)
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  givenname: Artur S. C
  orcidid: 0000-0002-1108-2123
  surname: Rego
  fullname: Rego, Artur S. C
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  givenname: Amanda L. T
  orcidid: 0000-0001-7602-8980
  surname: Brandão
  fullname: Brandão, Amanda L. T
  email: amanda.lemette@puc-rio.br
  organization: Pontifı́cia Universidade Católica do Rio de Janeiro
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Snippet Kinetic Monte Carlo (MC) is the main stochastic strategy used to simulate polymerization systems, as it gives good results with simple formulation. Normally,...
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SubjectTerms algorithms
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process design
Python
Title General Method for Speeding Up Kinetic Monte Carlo Simulations
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