Abstract
Artificial Intelligence techniques have been employed for the first time to train a dataset of 1258 distinct fluoride glasses collected from published literature to predict the density of novel oxy-fluoro glasses based on their chemical composition and ionic radii. The glass dataset was split based on the linear and non-linear variation to predict the glass density using various AI models like gradient descent, random forest regression and artificial neural networks. High concentration of boron in glass specimens resulted in scattering of datapoints in packing factor relation and density prediction. The random forest regression model fit the combined glass dataset with the highest R2 of 0.980. In case of boron-rich glasses, their non-linear behavior restricted the R2 for ANNs to 0.792 as optimum with the tanh activation function.
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