Thesis-2006-Bholoa.pdf (9.64 MB)
Potential energy surfaces using neural networks
thesis
posted on 2018-10-09, 11:51 authored by Ajeevsing BholoaA neural network is developed to fit a potential energy surface of silicon
derived from Frauenheim tight-binding data for silicon. The tight-binding
method retains the essentials of quantum mechanics for electronic structure
calculations but is faster to calculate than a full ab initio model.
The development of the neural network potential energy surface was carried
out by a progressive refinement of the design parameters. The refinement
of the models went hand in hand with the difficulty encountered in developing
a transferable network potential. Both equilibrium and non-equilibrium
parts of the potential energy surface were represented in the training data
set. The neural network potential was fitted on dimers, linear and angled
trimers, tetramers, diamond structures, distorted diamond lattice systems,
and the BC8, ST12, BCT5 and β-tin structures. [Continues.]
Funding
Loughborough University research studentship. Great Britain, Government (overseas research studentship).
History
School
- Science
Department
- Mathematical Sciences
Publisher
© Ajeevsing BholoaPublisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Publication date
2006Notes
A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy at Loughborough University.Language
- en