Search for: All records

Abstract Semiconductor materials provide a compelling platform for quantum technologies (QT). However, identifying promising material hosts among the plethora of candidates is a major challenge. Therefore, we have developed a framework for the automated discovery of semiconductor platforms for QT using material informatics and machine learning methods. Different approaches were implemented to label data for training the supervised machine learning (ML) algorithms logistic regression, decision trees, random forests and gradient boosting. We find that an empirical approach relying exclusively on findings from the literature yields a clear separation between predicted suitable and unsuitable candidates. In contrast to expectations from the literature focusing on band gap and ionic character as important properties for QT compatibility, the ML methods highlight features related to symmetry and crystal structure, including bond length, orientation and radial distribution, as influential when predicting a material as suitable for QT. 

The electric field dependence of emission rate of the deep traps with level near Ec−0.6 eV, so-called E1 traps, was studied by means of deep level transient spectroscopy measurements over a wide range of applied voltages. The traps were initially introduced by 900 °C ampoule annealing in molecular hydrogen. The results indicate the activation energy of the centers and the ratio of high-field to low-field electron emission rates at a fixed temperature scale as the square root of electric field, suggesting that the centers behave as deep donors. The possible microscopic nature of the centers in view of recent theoretical calculations is discussed. The most likely candidates for the E1 centers are SiGa1–H or SnGa2–H complexes.