Can superconductivity be predicted with the aid of pattern recognition techniques ?

Publisher Website
Google Scholar
Abstract
Pattern recognition techniques were employed in order to investigate the possibility to find features of the elements of the periodic system that may be relevant for the description of their behaviour with respect to superconductivity. Learning machines were constructed using those elements of the periodic system whose superconducting properties have been well studied. Relevant features appear to be the electronic work function and the number of valence electrons as given by Miedema, the specific heat, the heat of melting, the heat of sublimation, the melting point and the atomic radius. The learning machines have a predicting capability of the order of 90 %. The predictive power of these machines concerning the superconducting behaviour of the alkali and alkaline-earth metals belonging to a given test set, however, appears to be less convincing

This publication has 4 references indexed in Scilit: