### Abstract

Original language | English |
---|---|

Pages (from-to) | 1143-1154 |

Number of pages | 12 |

Journal | Journal of Computational Chemistry |

Volume | 34 |

Issue number | 13 |

DOIs | |

Publication status | Published - 2013 |

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*Journal of Computational Chemistry*,

*34*(13), 1143-1154. https://doi.org/10.1002/jcc.23246

}

*Journal of Computational Chemistry*, vol. 34, no. 13, pp. 1143-1154. https://doi.org/10.1002/jcc.23246

**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.

Research output: Contribution to journal › Article › Academic › peer-review

TY - JOUR

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

N2 - 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.

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.

U2 - 10.1002/jcc.23246

DO - 10.1002/jcc.23246

M3 - Article

C2 - 23420666

VL - 34

SP - 1143

EP - 1154

JO - Journal of Computational Chemistry

JF - Journal of Computational Chemistry

SN - 0192-8651

IS - 13

ER -