Zobrazeno 1 - 10
of 146
pro vyhledávání: '"Conduit G"'
Autor:
Foo, D. C. W., Conduit, G. J.
Publikováno v:
Phys. Rev. A 100, 063602 (2019)
We probe the superconducting gap in the zero temperature ground state of an attractively interacting spin-imbalanced two-dimensional Fermi gas with Diffusion Monte Carlo. A condensate fraction at nonzero pair momentum evidences a spatially non-unifor
Externí odkaz:
http://arxiv.org/abs/1910.13582
Autor:
Dann, J. R. A., Verpoort, P. C., de Oliveira, J. Ferreira, Rowley, S. E., Ford, C. J. B., Conduit, G. J., Narayan, V.
Publikováno v:
Phys. Rev. Applied 12, 034024 (2019)
We present results of a Au-Ge alloy that is useful as a resistance-based thermometer from room temperature down to at least \SI{0.2}{\kelvin}. Over a wide range, the electrical resistivity of the alloy shows a logarithmic temperature dependence, whic
Externí odkaz:
http://arxiv.org/abs/1902.10111
Autor:
Narayan, V., Verpoort, P. C., Dann, J. R. A., Backes, D., Ford, C. J. B., Lanius, M., Jalil, A. R., Schüffelgen, P., Mussler, G., Conduit, G. J., Grützmacher, D.
Publikováno v:
Phys. Rev. B 100, 024504 (2019)
We report non-equilibrium magnetodynamics in the Rashba-superconductor GeTe, which lacks inversion symmetry in the bulk. We find that at low temperature the system exhibits a non-equilibrium state, which decays on time scales that exceed conventional
Externí odkaz:
http://arxiv.org/abs/1902.04675
Publikováno v:
Materials & Design 131, 358 (2017)
A new computational tool has been developed to model, discover, and optimize new alloys that simultaneously satisfy up to eleven physical criteria. An artificial neural network is trained from pre-existing materials data that enables the prediction o
Externí odkaz:
http://arxiv.org/abs/1803.03039
Publikováno v:
Scripta Materialia 146, 82 (2018)
An artificial intelligence tool is exploited to discover and characterize a new molybdenum-base alloy that is the most likely to simultaneously satisfy targets of cost, phase stability, precipitate content, yield stress, and hardness. Experimental te
Externí odkaz:
http://arxiv.org/abs/1803.00879
Publikováno v:
Computational Materials Science 147, 176-185 (2018)
We apply an artificial neural network to model and verify material properties. The neural network algorithm has a unique capability to handle incomplete data sets in both training and predicting, so it can regard properties as inputs allowing it to e
Externí odkaz:
http://arxiv.org/abs/1803.00133
Autor:
Whitehead, T. M., Conduit, G. J.
Weak attractive interactions in a spin-imbalanced Fermi gas induce a multi-particle instability, binding multiple fermions together. The maximum binding energy per particle is achieved when the ratio of the number of up- and down-spin particles in th
Externí odkaz:
http://arxiv.org/abs/1712.09847
Publikováno v:
Physical Review A 96, 023619 (2017)
The Feshbach resonance provides precise control over the scattering length and effective range of interactions between ultracold atoms. We propose the ultratransferable pseudopotential to model effective interaction ranges $-1.5 \leq k_\mathrm{F}^2 R
Externí odkaz:
http://arxiv.org/abs/1708.09348
Autor:
Schonenberg, L. M., Conduit, G. J.
Publikováno v:
Phys. Rev. A 95, 013633 (2017)
A Fermi gas of cold atoms allows precise control over the dimensionless effective range, $k_\mathrm{F} R_\mathrm{eff}$, of the Feshbach resonance. Our pseudopotential formalism allows us to create smooth potentials with effective range, $-2 \leq k_\m
Externí odkaz:
http://arxiv.org/abs/1702.01583
Publikováno v:
Physical Review B 94 035157 (2016)
We propose a Jastrow factor for electron-electron correlations that interpolates between the radial symmetry of the Coulomb interaction at short inter-particle distance and the space-group symmetry of the simulation cell at large separation. The prop
Externí odkaz:
http://arxiv.org/abs/1607.05921