Journal
COMPUTATIONAL AND THEORETICAL CHEMISTRY
Volume 975, Issue 1-3, Pages 42-51Publisher
ELSEVIER
DOI: 10.1016/j.comptc.2011.04.004
Keywords
Quantum chemical topology; Force field; Multipole moment; Polarisation; Atoms in molecules; Machine learning
Categories
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available