J. Phys. France 43, 97-106 (1982)
DOI: 10.1051/jphys:0198200430109700

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

F.W. Pijpers¹ et G. Vertogen<sup>2

1</sup>  Department of Analytical Chemistry, Catholic University, Toernooiveld, Nijmegen, The Netherlands
²  Institute for Theoretical Physics, Catholic University, Toernooiveld, Nijmegen, The Netherlands

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.

Résumé
Nous employons une technique de reconnaissance de corrélations pour essayer de trouver des caractères communs aux éléments supraconducteurs dont les propriétés déjà connues servent de base de départ. Les caractéristiques essentielles sont : le travail de sortie électronique et le nombre d'électrons de valence selon Miedema, la chaleur spécifique, la chaleur de fusion et de sublimation, le point de fusion et le rayon atomique. Nos prévisions sont valables à 90 %. Cependant en ce qui concerne les alcalins et les alcalino-terreux d'un groupe donné, nos prévisions sont moins convaincantes.

PACS
7410 - Occurrence, potential candidates.

Key words
pattern recognition -- superconductivity