  Semiconductor materials provide a compelling platform for quantum
technologies (QT), and the properties of a vast amount of materials can be
found in databases containing information from both experimental and
theoretical explorations. However, searching these databases to find promising
candidate materials for quantum technology applications is a major challenge.
Therefore, we have developed a framework for the automated discovery of
semiconductor host platforms for QT using material informatics and machine
learning methods, resulting in a dataset consisting of over $25.000$ materials
and nearly $5000$ physics-informed features. Three approaches were devised,
named the Ferrenti, extended Ferrenti and the empirical approach, to label data
for the supervised machine learning (ML) methods logistic regression, decision
trees, random forests and gradient boosting. We find that of the three, the
empirical approach relying exclusively on findings from the literature
predicted substantially fewer candidates than the other two approaches with a
clear distinction between suitable and unsuitable candidates when comparing the
two largest eigenvalues in the covariance matrix. In contrast to expectations
from the literature and that found for the Ferrenti and extended Ferrenti
approaches focusing on band gap and ionic character, the ML methods from the
empirical approach highlighted features related to symmetry and crystal
structure, including bond length, orientation and radial distribution, as
influential when predicting a material as suitable for QT. All three approaches
and all four ML methods agreed on a subset of $47$ eligible candidates %(to a
probability of $>50 \ \%$) of $8$ elemental, $29$ binary, and $10$ tertiary
compounds, and provide a basis for further material explorations towards
quantum technology.
