Line of Dirac monopoles embedded in a Bose-Einstein condensate

The gauge field of a uniform line of magnetic monopoles is created using a single Laguerre-Gauss laser mode and a gradient in the physical magnetic field. We study the effect of these monopoles on a Bose condensed atomic gas, whose vortex structure transforms when more than six monopoles are trapped within the cloud. Finally, we study this transition with the collective modes

Predicting physical properties of alkanes with neural networks

We train artificial neural networks to predict the physical properties of linear, single branched, and double branched alkanes. These neural networks can be trained from fragmented data, which enables us to use physical property information as inputs and exploit property-property correlations to improve the quality of our predictions. We characterize every alkane uniquely using a set of five chemical descriptors. We establish correlations between branching and the boiling point, heat capacity, and vapor pressure as a function of temperature. We establish how the symmetry affects the melting point and identify erroneous data entries in the flash point of linear alkanes. Finally, we exploit the temperature and pressure dependence of shear viscosity and density in order to model the kinematic viscosity of linear alkanes. The accuracy of the neural network models compares favorably to the accuracy of several physico-chemical/thermodynamic methods