Parameterization of a reactive force field using a Monte Carlo algorithm

E. Iype, M. Hutter, A.P.J. Jansen, S.V. Nedea, C.C.M. Rindt

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Abstract

Parameterization of a molecular dynamics force field is essential in realistically modeling the physicochemical processes involved in a molecular system. This step is often challenging when the equations involved in describing the force field are complicated as well as when the parameters are mostly empirical. ReaxFF is one such reactive force field which uses hundreds of parameters to describe the interactions between atoms. The optimization of the parameters in ReaxFF is done such that the properties predicted by ReaxFF matches with a set of quantum chemical or experimental data. Usually, the optimization of the parameters is done by an inefficient single-parameter parabolic-search algorithm. In this study, we use a robust metropolis Monte-Carlo algorithm with simulated annealing to search for the optimum parameters for the ReaxFF force field in a high-dimensional parameter space. The optimization is done against a set of quantum chemical data for MgSO4 hydrates. The optimized force field reproduced the chemical structures, the equations of state, and the water binding curves of MgSO4 hydrates. The transferability test of the ReaxFF force field shows the extend of transferability for a particular molecular system. This study points out that the ReaxFF force field is not indefinitely transferable.
Original languageEnglish
Pages (from-to)1143-1154
Number of pages12
JournalJournal of Computational Chemistry
Volume34
Issue number13
DOIs
Publication statusPublished - 2013

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Monte Carlo Algorithm
Force Field
Parameterization
Hydrates
Simulated annealing
Equations of state
Molecular dynamics
Optimization
Atoms
Water
Metropolis Algorithm
Simulated Annealing
Equation of State
Molecular Dynamics
Search Algorithm
Parameter Space
High-dimensional
Experimental Data
Curve
Interaction

Cite this

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title = "Parameterization of a reactive force field using a Monte Carlo algorithm",
abstract = "Parameterization of a molecular dynamics force field is essential in realistically modeling the physicochemical processes involved in a molecular system. This step is often challenging when the equations involved in describing the force field are complicated as well as when the parameters are mostly empirical. ReaxFF is one such reactive force field which uses hundreds of parameters to describe the interactions between atoms. The optimization of the parameters in ReaxFF is done such that the properties predicted by ReaxFF matches with a set of quantum chemical or experimental data. Usually, the optimization of the parameters is done by an inefficient single-parameter parabolic-search algorithm. In this study, we use a robust metropolis Monte-Carlo algorithm with simulated annealing to search for the optimum parameters for the ReaxFF force field in a high-dimensional parameter space. The optimization is done against a set of quantum chemical data for MgSO4 hydrates. The optimized force field reproduced the chemical structures, the equations of state, and the water binding curves of MgSO4 hydrates. The transferability test of the ReaxFF force field shows the extend of transferability for a particular molecular system. This study points out that the ReaxFF force field is not indefinitely transferable.",
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Parameterization of a reactive force field using a Monte Carlo algorithm. / Iype, E.; Hutter, M.; Jansen, A.P.J.; Nedea, S.V.; Rindt, C.C.M.

In: Journal of Computational Chemistry, Vol. 34, No. 13, 2013, p. 1143-1154.

Research output: Contribution to journalArticleAcademicpeer-review

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T1 - Parameterization of a reactive force field using a Monte Carlo algorithm

AU - Iype, E.

AU - Hutter, M.

AU - Jansen, A.P.J.

AU - Nedea, S.V.

AU - Rindt, C.C.M.

PY - 2013

Y1 - 2013

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AB - Parameterization of a molecular dynamics force field is essential in realistically modeling the physicochemical processes involved in a molecular system. This step is often challenging when the equations involved in describing the force field are complicated as well as when the parameters are mostly empirical. ReaxFF is one such reactive force field which uses hundreds of parameters to describe the interactions between atoms. The optimization of the parameters in ReaxFF is done such that the properties predicted by ReaxFF matches with a set of quantum chemical or experimental data. Usually, the optimization of the parameters is done by an inefficient single-parameter parabolic-search algorithm. In this study, we use a robust metropolis Monte-Carlo algorithm with simulated annealing to search for the optimum parameters for the ReaxFF force field in a high-dimensional parameter space. The optimization is done against a set of quantum chemical data for MgSO4 hydrates. The optimized force field reproduced the chemical structures, the equations of state, and the water binding curves of MgSO4 hydrates. The transferability test of the ReaxFF force field shows the extend of transferability for a particular molecular system. This study points out that the ReaxFF force field is not indefinitely transferable.

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