Use this URL to cite or link to this record in EThOS: | https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.496512 |
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Title: | Polarization for molecular simulation from a multilayer perceptron trained by ab initio electron densities of clusters | ||||
Author: | Handley, Christopher M. |
ISNI:
0000 0004 2668 6469
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Awarding Body: | MANCHESTER UNIVERSITY | ||||
Current Institution: | University of Manchester | ||||
Date of Award: | 2009 | ||||
Availability of Full Text: |
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Abstract: | |||||
It is widely accepted that correctly accounting for polarization within simulations involving water is critical if the structural, dynamic and thermodynamic properties of such systems are to be accurately reproduced. We propose a novel potential for the water dimer, trimer, tetramer, pentamer, hexamer that includes polarization explicitly, for use in Molecular Dynamics simulations. Using thousands of dimer, trimer, tetramer, pentamer and hexamer clusters sampled from a Molecular Dynamics simulation lacking polarization, we train (Artificial) Neural Networks (NNs) (also known as multilayer perceptrons), and other machine learning methods, to predict the atomic multipole moments of a central water molecule.
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Supervisor: | Not available | Sponsor: | Not available | ||
Qualification Name: | Thesis (Ph.D.) | Qualification Level: | Doctoral | ||
EThOS ID: | uk.bl.ethos.496512 | DOI: | Not available | ||
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