@article{VITORIA2024122870,
title = {Using a simple radial basis function neural network to predict the glass transition temperature of alkali borate glasses},
author = {Leonardo Santos Vitoria and Daniel R. Cassar and Susana Souza Lalic and Marcio Luis Ferreira Nascimento},
url = {https://www.sciencedirect.com/science/article/pii/S0022309324000516},
doi = {https://doi.org/10.1016/j.jnoncrysol.2024.122870},
issn = {0022-3093},
year = {2024},
date = {2024-02-17},
urldate = {2024-01-01},
journal = {Journal of Non-Crystalline Solids},
volume = {629},
pages = {122870},
abstract = {Artificial Neural Networks (ANN) are powerful machine learning algorithms. In the context of glass science, ANNs have been successfully used to induce composition-property regression models. These models are already influencing the way that we design new glasses, moving away from the traditional “cook and look” approach to computer-aided design. In this work, we induced an ANN model for the glass transition temperature Tg for glasses of the family xM2O⋅(100−x)B2O3, where M = Li, Na, K, Rb, and Cs, using simple radial basis function neural networks (RBF) with only two neurons. Statistically, the results of our methodologies are comparable to other complex models (empirical or physical) available in the literature with more neurons, such as the recent GlassNet and the topological model proposed by Mauro and co-authors (2009). This result supports the idea that a simpler specialized model can perform better than a complex general model for a restricted training domain.},
keywords = {Artificial neural network, Glass transition temperature, Radial basis function, Simple neural network},
pubstate = {published},
tppubtype = {article}
}
