物理化学学报 >> 2009, Vol. 25 >> Issue (07): 1439-1442.doi: 10.3866/PKU.WHXB20090743

研究论文 上一篇    下一篇

一种确定反应中间态几何特征和能量的综合性方法

郑铮, 刘振明, 张亮仁   

  1. 北京大学药学院药物化学系, 天然药物及仿生药物国家重点实验室, 北京 100191
  • 收稿日期:2009-02-16 修回日期:2009-04-14 发布日期:2009-06-26
  • 通讯作者: 刘振明 E-mail:zmliu@bjmu.edu.cn

A Combined Method for Determining Reaction Transition State Geometry and Energy

ZHENG Zheng, LIU Zhen-Ming, ZHANG Liang-Ren   

  1. State Key Laboratory of Natural and Biomimetic Drugs, Department of Medicinal Chemistry, School of Pharmaceutical Sciences, Peking University, Beijing 100191, P. R. China
  • Received:2009-02-16 Revised:2009-04-14 Published:2009-06-26
  • Contact: LIU Zhen-Ming E-mail:zmliu@bjmu.edu.cn

摘要:

通过综合使用传统的过渡态优化算法、数学统计工具以及人工神经网络算法(ANN)找到一种不依赖于反应物起始构象而得到化学反应中过渡态结构和能量的方法. 在两个反应物互相接近的过程中, 每一步的几何构象都对应着一个系统能量值. 本研究的目的是尽可能地收集处在反应能量面上的这种能量点值. 通过采用几何参数作为自变量对势能面进行模拟研究, 得到了势能面上对应过渡态结构的一阶鞍点. 采用乙醛负离子和甲醛作为反应物, 对经典的醛醇缩合反应中的亲核进攻步骤进行了研究. 对内禀反应坐标(IRC)路径的计算是从反应物的三组不同起始构象出发, 最终获得了反应势能面上的96个点. 本研究中的势能面采用人工神经网络算法进行模拟研究, 并利用交叉验证方法评估得到的结果, 避免了采用人工神经网络算法时过度拟合情况的发生.

关键词: 反应过渡态, 反应物几何构象, 人工神经网络, 反应势能面, 一阶鞍点, 交叉验证

Abstract:

We found an alternative method for the derivation of transition state structure energy in chemical reactions which would be less dependent on the starting geometry of reactants by combining a mathematical tool and artificial neural networks (ANN) with conventional transition state optimization algorithms. When two reactants approach each other, every geometric structure corresponds to a system energy value. The purpose of this investigation was to collect as many energy values on the reaction energy surface as possible. By simulating the energy surface using the geometric parameters as independent variables, the first order saddle point in the energy surface corresponding to the transition state structure was derived. The nucleophilic attack step of a classical Aldol reaction was studied using acetaldehyde anion and formaldehyde as reactants. The intrinsic reaction coordinate (IRC) path calculation started with 3 different sets of starting reactant geometries and 96 points on the reaction energy surface were derived. The energy surface was simulated using ANN. Cross-validation was applied to evaluate the result and avoided a possible overfitting of the ANN.

Key words: Reaction transition state, Geometry of reactant, Artificial neural network, Reaction energy surface, First order saddle point, Cross-validation

MSC2000: 

  • O641