Parameter optimization in molecular dynamics simulations using a genetic algorithm

Angibaud, L.; Briquet, Ludovic; Philipp, Patrick; Wirtz, Tom; Kieffer, John

doi:10.1016/j.nimb.2010.11.024

Cited by 19 publications

(17 citation statements)

References 17 publications

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“…Parameters in a specialized charge‐transfer force field were optimized with a GA . Pahari and Chaturvedi optimized ReaxFF parameters with a GA, but the focus of their paper was on determining a minimal set of parameters to vary in the GA based on prior sensitivity tests and cross‐correlations.…”

Section: Related Workmentioning

confidence: 99%

“…The parameter optimization problem for reactive force fields is harder than that of traditional force fields, because there are far more parameters per atom, these parameters are more strongly coupled, a significantly larger reference data set is needed, and we have limited knowledge about the relationship between reference data items and force field parameters. GA methods have successfully been applied to this challenging task, including a GA optimization study of ReaxFF parameters for SiOH and azobenzene by one of the present authors.…”

Section: Introductionmentioning

confidence: 99%

“…The techniques used in Ref. are single‐objective GA optimization techniques. Recently, a number of studies using multi‐objective GA techniques to optimize ReaxFF parameters were published (see Section Related Work).…”

Section: Introductionmentioning

confidence: 99%

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Efficient global optimization of reactive force‐field parameters

et al. 2015

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Reactive force fields make low-cost simulations of chemical reactions possible. However, optimizing them for a given chemical system is difficult and time-consuming. We present a high-performance implementation of global force-field parameter optimization, which delivers parameter sets of the same quality with much less effort and in far less time than before, and also offers excellent parallel scaling. We demonstrate these features with example applications targeting the ReaxFF force field. © 2015 Wiley Periodicals, Inc.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient global optimization of reactive force‐field parameters

et al. 2015

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“…The parameter set describing the Si-Si interaction is available in the literature [16,17]. The parameter sets for C-C and Si-C interactions will be presented in the next paragraphs.…”

Section: Kieffer Interatomic Potentialmentioning

confidence: 99%

“…Kieffer's force field is newly developed and allows for the possibility to account for the breaking and formation of bonds as well as for a dynamic charge transfer between pairs of atoms. A Kieffer force field parameter set is available in the literature for Si-Si interactions [16,17], but is missing for Si-C and C-C interactions. The first part of this paper therefore presents a newly derived parameter set for the C-C and C-Si interactions, suitable for Kieffer's force field.…”

Section: Introductionmentioning

confidence: 99%

Reactive force field potential for carbon deposition on silicon surfaces

Briquet¹,

Jana²,

Mether

et al. 2012

J. Phys.: Condens. Matter

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In this paper a new interatomic potential based on the Kieffer force field and designed to perform molecular dynamics (MD) simulations of carbon deposition on silicon surfaces is implemented. This potential is a third-order reactive force field that includes a dynamic charge transfer and allows for the formation and breaking of bonds. The parameters for Si-C and C-C interactions are optimized using a genetic algorithm. The quality of the potential is tested on its ability to model silicon carbide and diamond physical properties as well as the formation energies of point defects. Furthermore, MD simulations of carbon deposition on reconstructed (100) silicon surfaces are carried out and compared to similar simulations using a Tersoff-like bond order potential. Simulations with both potentials produce similar results showing the ability to extend the use of the Kieffer potential to deposition studies. The investigation reveals the presence of a channelling effect when depositing the carbon at 45° incidence angle. This effect is due to channels running in directions symmetrically equivalent to the (110) direction. The channelling is observed to a lesser extent for carbon atoms with 30° and 60° incidence angles relative to the surface normal. On a pristine silicon surface, sticking coefficients were found to vary between 100 and 73%, depending on deposition conditions.

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Global optimization of parameters in the reactive force field ReaxFF for SiOH

2013

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We have used unbiased global optimization to fit a reactive force field to a given set of reference data. Specifically, we have employed genetic algorithms (GA) to fit ReaxFF to SiOH data, using an in-house GA code that is parallelized across reference data items via the message-passing interface (MPI). Details of GA tuning turn-ed out to be far less important for global optimization efficiency than using suitable ranges within which the parameters are varied. To establish these ranges, either prior knowledge can be used or successive stages of GA optimizations, each building upon the best parameter vectors and ranges found in the previous stage. We have finally arrive-ed at optimized force fields with smaller error measures than those published previously. Hence, this optimization approach will contribute to converting force-field fitting from a specialist task to an everyday commodity, even for the more difficult case of reactive force fields.

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Parameter optimization in molecular dynamics simulations using a genetic algorithm

Cited by 19 publications

References 17 publications

Efficient global optimization of reactive force‐field parameters

Efficient global optimization of reactive force‐field parameters

Reactive force field potential for carbon deposition on silicon surfaces

Global optimization of parameters in the reactive force field ReaxFF for SiOH

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