雷亚克夫
力场(虚构)
分子动力学
模拟退火
蒙特卡罗方法
可转让性
量子退火
统计物理学
领域(数学)
化学
量子
计算化学
计算机科学
算法
物理
数学
原子间势
量子力学
量子计算机
人工智能
统计
罗伊特
机器学习
纯数学
作者
Eldhose Iype,Markus Hütter,A. P. J. Jansen,S. V. Nedea,C.C.M. Rindt
摘要
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 MgSO 4 hydrates. The optimized force field reproduced the chemical structures, the equations of state, and the water binding curves of MgSO 4 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. © 2013 Wiley Periodicals, Inc.
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