Suppr超能文献

使用混合反向蒙特卡罗方法对多孔碳进行分子建模。

Molecular modeling of porous carbons using the hybrid reverse Monte Carlo method.

作者信息

Jain Surendra K, Pellenq Roland J-M, Pikunic Jorge P, Gubbins Keith E

机构信息

Center for High Performance Simulation and Department of Chemical and Biomolecular Engineering, North Carolina State University at Raleigh, Box 7905, Raleigh, North Carolina 27695-7905, USA.

出版信息

Langmuir. 2006 Nov 21;22(24):9942-8. doi: 10.1021/la053402z.

Abstract

We apply a simulation protocol based on the reverse Monte Carlo (RMC) method, which incorporates an energy constraint, to model porous carbons. This method is called hybrid reverse Monte Carlo (HRMC), since it combines the features of the Monte Carlo and reverse Monte Carlo methods. The use of the energy constraint term helps alleviate the problem of the presence of unrealistic features (such as three- and four-membered carbon rings), reported in previous RMC studies of carbons, and also correctly describes the local environment of carbon atoms. The HRMC protocol is used to develop molecular models of saccharose-based porous carbons in which hydrogen atoms are taken into account explicitly in addition to the carbon atoms. We find that the model reproduces the experimental pair correlation function with good accuracy. The local structure differs from that obtained with a previous model (Pikunic, J.; Clinard, C.; Cohaut, N.; Gubbins, K. E.; Guet, J. M.; Pellenq, R. J.-M.; Rannou, I.; Rouzaud, J. N. Langmuir 2003, 19 (20), 8565). We study the local structure by calculating the nearest neighbor distribution, bond angle distribution, and ring statistics.

摘要

我们应用基于反向蒙特卡罗(RMC)方法的模拟协议来模拟多孔碳,该方法纳入了能量约束。这种方法被称为混合反向蒙特卡罗(HRMC),因为它结合了蒙特卡罗方法和反向蒙特卡罗方法的特点。能量约束项的使用有助于缓解先前碳的RMC研究中报道的存在不现实特征(如三元和四元碳环)的问题,并且还能正确描述碳原子的局部环境。HRMC协议用于开发蔗糖基多孔碳的分子模型,其中除了碳原子外还明确考虑了氢原子。我们发现该模型能以良好的精度再现实验对关联函数。其局部结构与先前模型(Pikunic, J.; Clinard, C.; Cohaut, N.; Gubbins, K. E.; Guet, J. M.; Pellenq, R. J.-M.; Rannou, I.; Rouzaud, J. N. Langmuir 2003, 19 (20), 8565)所得到的结构不同。我们通过计算最近邻分布、键角分布和环统计来研究局部结构。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验