Artificial-neural-network prediction of hexagonal lattice parameters for non-stoichiometric apatites

Date

2014-01-01

Author

Kockan, Umit
Ozturk, Fahrettin
Evis, Zafer

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

3
views

0
downloads

In this study, hexagonal lattice parameters (a and c) and unit-cell volumes of non-stoichiometric apatites of M-10(TO4)(6)X-2 are predicted from their ionic radii with artificial neural networks. A multilayer-perceptron network is used for training. The results indicate that the Bayesian regularization method with four neurons in the hidden layer with a tansig activation function and one neuron in the output layer with a purelin function gives the best results. It is found that the errors for the predicted data of the lattice parameters of a and c are less than 1 % and 2 %, respectively. On the other hand, about 3 % errors were encountered for both lattice parameters of the non-stoichiometric apatites with exact formulas in the presence of the T-site ions that are not used for training the artificial neural network.

Subject Keywords

Artificial neural networks, Crystal structure, Hydroxyapatite, Multilayer-perceptron network

URI

https://hdl.handle.net/11511/54827

Collections

Department of Engineering Sciences, Article