+44 (0)1509 263171
Please use this identifier to cite or link to this item:
|Title: ||A new approach to potential fitting using neural networks|
|Authors: ||Bholoa, A.|
Kenny, Steven D.
|Keywords: ||Neural network|
|Issue Date: ||2007|
|Publisher: ||© Elsevier|
|Citation: ||BHOLOA, A., KENNY, S.D. and SMITH, R., 2007. A new approach to potential fitting using neural networks. Nuclear instruments and methods in physics research section B: Beam interactions with materials and atoms, 255 (1), pp. 1-7 [doi:10.1016/j.nimb.2006.11.040]|
|Abstract: ||A methodology is presented for developing transferable empirical
potential functions without following the usual procedure of postulating
a functional form. Instead, a neural network (NN) is employed
to learn the functional relationships of potential energy surfaces from
the local geometric arrangement of atoms. The methodology is illustrated
by training the NN model on tens of thousands of individual
data points derived from the tight-binding (TB) method for a wide
range of silicon systems including both small clusters and bulk structures.
Comparisons of the potential’s properties with experimental
data, quantum methods and other Si potentials have been made.
The NN model successfully fitted energy variations of the different
test cases as a function of bond distances, bond angles, lattice constants
and elastic properties for both equilibrium and non-equilibrium
small cluster and bulk structures. This indicates a robust and consistent
methodology for fitting empirical potentials which can be applied
to a wide range of materials independent of the type of bonding or
their crystal structure.|
|Description: ||This article has been accepted for publication in the journal, Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms
[© Elsevier] and the definitive version is available at: http://www.sciencedirect.com/science/journal/0168583X|
|Appears in Collections:||Published Articles (Maths)|
Files associated with this item:
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.