A machine learning approach to the estimation of the liquidus temperature of glass-forming oxide blends

Many properties of glasses and glass-forming liquids of oxide mixtures vary in a relatively simple and regular way with the oxide concentrations. In that respect, the liquidus temperature is an exception, which makes its prediction difficult: the surface to be estimated is fairly complex, so that usual regression methods involve a large number of adjustable parameters. Neural networks, viewed as parameterized non-linear regression functions, were proved to be parsimonious: in order to reach the same prediction accuracy, a neural network requires a smaller number of adjustable parameters than conventional regression techniques such as polynomial regression. We demonstrate this very valuable property on some examples of oxide mixtures involving up to five components. In the latter case, we show that neural networks provide a sizeable improvement over polynomial methods.