Multi-objective optimisation on the basis of random models for ethylene oxide

This paper is part of our pursuit to develop an efficient procedure for optimising parameters that provide a reliable foundation for highly predictive molecular simulations. We tested whether DesParO, a mathematical tool originally used in automotive design, is suitable for creating Lennard-Jones (L...

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Bibliographic Details
Published in:Molecular simulation 2010-12, Vol.36 (15), p.1208-1218
Main Authors: Maaß, Astrid, Nikitina, Lialia, Clees, Tanja, Kirschner, Karl N., Reith, Dirk
Format: Article
Language:English
Subjects:
Automotive components
Computer simulation
DesParO
Ethylene oxide
force field
GROW
Interpolation
Mathematical models
meta-model
Monte Carlo methods
Optimization
parameter optimisation
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Summary:This paper is part of our pursuit to develop an efficient procedure for optimising parameters that provide a reliable foundation for highly predictive molecular simulations. We tested whether DesParO, a mathematical tool originally used in automotive design, is suitable for creating Lennard-Jones (LJ) parameters that accurately reproduce the experimental phase behaviour for our test compound ethylene oxide (EO). So, we created a multitude of diverse random parameter sets, performed Gibbs ensemble Monte Carlo simulations and collected the resulting physical properties. On that data basis, DesParO derived a meta-model through a multidimensional interpolation. We then explored, in an interactive fashion unique to DesParO, the LJ parameter space and selected some suitable parameter sets, which were then tested by simulations. For EO, the selected parameter sets were indeed superior to the initial parameters. Furthermore, the new parameters can be reliably used as input for further optimisation by other methods, resulting in extremely robust LJ parameters. Beyond the prediction of parameter sets, DesParO enabled us to examine the underlying parameter-property relationships that help us solve future optimisation problems by creating subordinate parameter optimisation tasks in a systematic manner; this ability makes DesParO a valuable tool in the overall optimisation process.
ISSN:0892-7022
1029-0435
DOI:10.1080/08927020903483312