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 prarmeters. Neutral networks, viewed as parametrerized non-linear regression functions, were proved to be parsimonious: in order to reach the same prediction accuracy, a neutral network requires a smaller number of adjustable parameters than conventional regression techniques such as polynomial regression. This article demonstrates this valuable property on some examples of oxide mixtures involving up to five components.
