Modeling Electronic Quantum Transport with Machine Learning

We present a Machine Learning approach to solve electronic quantum transport equations of one-dimensional nanostructures. The transmission coefficients of disordered systems were computed to provide training and test datasets to the machine. The system's representation encodes energetic as well as geometrical information to characterize similarities between disordered configurations, while the Euclidean norm is used as a measure of similarity. Errors for out-of-sample predictions systematically decrease with training set size, enabling the accurate and fast prediction of new transmission coefficients. The remarkable performance of our model to capture the complexity of interference phenomena lends further support to its viability in dealing with transport problems of undulatory nature.Comment: 5 pages, 4 figure

Plentiful magnetic moments in oxygen deficient SrTiO3

Correlated band theory is employed to investigate the magnetic and electronic properties of different arrangements of oxygen di- and tri-vacancy clusters in SrTiO$_3$. Hole and electron doping of oxygen deficient SrTiO$_3$ yields various degrees of magnetization as a result of the interaction between localized magnetic moments at the defected sites. Different kinds of Ti atomic orbital hybridization are described as a function of the doping level and defect geometry. We find that magnetism in SrTiO$_{3-\delta}$ is sensitive to the arrangement of neighbouring vacancy sites, charge carrier density, and vacancy-vacancy interaction. Permanent magnetic moments in the absence of vacancy doping electrons are observed. Our description of the charged clusters of oxygen vacancies widens the previous descriptions of mono and multi-vacancies and points out the importance of the controled formation at the atomic level of defects for the realization of transition metal oxide based devices with a desirable magnetic performance.Comment: 6 pages, 5 figures, continuation of arXiv:1408.310