化学键
广义价键
价键理论
化学
共价键
债券定单
键能
泡利不相容原理
六重键
双原子分子
化学物理
计算化学
键离解能
价(化学)
粘结强度
现代价键理论
自然键轨道
粘结长度
轨道杂交
原子轨道
分子
离解(化学)
分子轨道
量子力学
物理
物理化学
电子
密度泛函理论
有机化学
胶粘剂
图层(电子)
作者
Gernot Frenking,F. Matthias Bickelhaupt
标识
DOI:10.1002/9783527664696.ch4
摘要
The basic principles of the energy decomposition analysis (EDA) and its combination with the natural orbital for chemical valence (EDA-NOCV) method are discussed. It is shown that the breakdown of the interaction energy of a chemical bond ΔEint into three major terms ΔEelstat, ΔEPauli and ΔEorb, which are uniquely defined in the EDA method is a very useful approach for understanding trends of bond strengths and bond lengths. The EDA results show that the stabilizing orbital interactions are not always the crucial terms for determining the trend of the bond strength. The electrostatic attraction which can be very large even in nonpolar bonds and the repulsive forces that come from the Pauli exclusion principle are in many cases decisive for understanding the strength of the interatomic interactions. An analysis of the bonding in the diatomic molecules Li2 – F2 shows that the equilibrium distance of covalent bonds is determined by the increase of the Pauli repulsion but not by the maximum overlap of the valence orbitals. An attractive feature of the EDA partitioning method is that the instantaneous interaction energy of a chemical bond is considered. The ΔEint values can be very different from the bond dissociation energys (BDEs) because the preparation energy of the interacting fragments may be large. The advancement to the EDA-NOCV method makes it possible to estimate the charge flow and the associated energy contribution of the pairwise orbital interactions. This makes the EDA a very powerful method for analyzing interatomic interactions at equilibrium structures or in transition states (TSs) which have only C1 symmetry. The EDA results can be interpreted in a plausible way which connects heuristic bonding models with the physical mechanism of the chemical bond.
科研通智能强力驱动
Strongly Powered by AbleSci AI