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多极极化和离子电荷转移的统一方法:微水合钠离子。

Unified approach to multipolar polarisation and charge transfer for ions: microhydrated Na+.

机构信息

Manchester Institute of Biotechnology (MIB), 131 Princess Street, Manchester M1 7DN, UK.

出版信息

Phys Chem Chem Phys. 2013 Nov 7;15(41):18249-61. doi: 10.1039/c3cp53204f.

DOI:10.1039/c3cp53204f
PMID:24064799
Abstract

Electrostatic effects play a large part in determining the properties of chemical systems. In addition, a treatment of the polarisation of the electron distribution is important for many systems, including solutions of monatomic ions. Typically employed methods for describing polarisable electrostatics use a number of approximations, including atom-centred point charges and polarisation methods that require iterative calculation on the fly. We present a method that treats charge transfer and polarisation on an equal footing. Atom-centred multipole moments describe the charge distribution of a chemical system. The variation of these multipole moments with the geometry of the surrounding atoms is captured by the machine learning method kriging. The interatomic electrostatic interaction can be computed using the resulting predicted multipole moments. This allows the treatment of both intra- and interatomic polarisation with the same method. The proposed method does not return explicit polarisabilities but instead, predicts the result of the polarisation process. An application of this new method to the sodium cation in a water environment is described. The performance of the method is assessed by comparison of its predictions of atomic multipole moments and atom-atom electrostatic interaction energies to exact results. The kriging models are able to predict the electrostatic interaction energy between the ion and all water atoms within 4 kJ mol(-1) for any of the external test set Na(+)(H2O)6 configurations.

摘要

静电效应在决定化学系统的性质方面起着重要作用。此外,对于许多系统,包括单原子离子溶液,对电子分布的极化的处理也很重要。通常用于描述极化静电的方法采用了许多近似,包括基于原子中心的点电荷和需要即时迭代计算的极化方法。我们提出了一种平等对待电荷转移和极化的方法。基于原子中心的多极矩描述了化学系统的电荷分布。这些多极矩随周围原子几何形状的变化可以通过机器学习方法克里金来捕捉。可以使用由此预测的多极矩来计算原子间的静电相互作用。这允许使用相同的方法处理原子内和原子间的极化。所提出的方法不返回显式极化率,而是预测极化过程的结果。将这种新方法应用于水环境中的钠离子进行了描述。通过将原子多极矩和原子-原子静电相互作用能的预测值与精确结果进行比较,评估了该方法的性能。克里金模型能够预测离子与所有水分子之间的静电相互作用能,对于外部测试集 Na(+)(H2O)6 的任何配置,其误差都在 4 kJ mol(-1)以内。

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