Intramolecular polarisable multipolar electrostatics from the machine learning method Kriging

被引：57

作者：

Mills, Matthew J. L.

Popelier, Paul L. A. ^{[1
]}

机构：

[1] MIB, Manchester M1 7DN, Lancs, England

来源：

COMPUTATIONAL AND THEORETICAL CHEMISTRY | 2011年 / 975卷 / 1-3期

基金：

英国工程与自然科学研究理事会;

关键词：

Quantum chemical topology; Force field; Multipole moment; Polarisation; Atoms in molecules; Machine learning; POTENTIAL-ENERGY SURFACES; FORCE-FIELD; NUCLEIC-ACIDS; LIQUID WATER; DYNAMICS; ATOM; REPRESENTATION; SIMULATION; PROTEINS; PARAMETERIZATION;

D O I：

10.1016/j.comptc.2011.04.004

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

We describe an intramolecularly polarisable multipolar electrostatic potential model for ethanol, which acts as a pilot molecule for this proof-of-concept study. We define atoms via the partitioning prescribed by quantum chemical topology (QCT). A machine learning method called Kriging is employed to capture the way atomic multipole moments vary upon conformational change. The multipole moments predicted by the Kriging models are used in the calculation of atom-atom electrostatic interaction energies. Charge transfer is treated in the same way as dipolar polarisation and the polarisation of higher rank multipole moments. This method enables the development of a new and more accurate force field. (C) 2011 Elsevier B.V. All rights reserved.

引用

页码：42 / 51

页数：10

共 67 条

[11] Beyond point charges: Dynamic polarization from neural net predicted multipole moments [J].