Artificial-neural-network prediction of hexagonal lattice parameters for non-stoichiometric apatites
Küçük Resim Yok
Tarih
2014
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this study, hexagonal lattice parameters (a and c) and unit-cell volumes of non-stoichiometric apatites of M10(TO4)6X2 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.
Açıklama
Anahtar Kelimeler
Artificial neural networks, Crystal structure, Hydroxyapatite, Multilayer-perceptron network
Kaynak
Materiali in Tehnologije
WoS Q Değeri
Scopus Q Değeri
Q3
Cilt
48
Sayı
1
