Topological states and quantized current in helical molecules

We report a theoretical study of electron transport along helical molecules under an external electric field, which is perpendicular to the helix axis of the molecule. Our results reveal that the topological states could appear in single-helical molecule and double-stranded DNA in the presence of the perpendicular electric field. And these topological states guarantee adiabatic charge pumping across the helical molecules by rotating the electric field in the transverse plane and the pumped current at zero bias voltage is quantized. In addition, the quantized current constitutes multiple plateaus by scanning the Fermi energy as well as the bias voltage, and hold for various model parameters, since they are topologically protected against perturbations. These results could motivate further experimental and theoretical studies in the electron transport through helical molecules, and pave the way to detect topological states and quantized current in the biological systems.Comment: 5 pages, 5 figure

Spin-Selective Transport of Electron in DNA Double Helix

The experiment that the high spin selectivity and the length-dependent spin polarization are observed in double-stranded DNA [Science ${\bf 331}$, 894 (2011)], is elucidated by considering the combination of the spin-orbit coupling, the environment-induced dephasing, and the helical symmetry. We show that the spin polarization in double-stranded DNA is significant even in the case of weak spin-orbit coupling, while no spin polarization appears in single-stranded DNA. Furthermore, the underlying physical mechanism and the parameters-dependence of the spin polarization are studied.Comment: 5 pages; 4 figure

Reveal flocking of birds flying in fog by machine learning

We study the first-order flocking transition of birds flying in low-visibility conditions by employing three different representative types of neural network (NN) based machine learning architectures that are trained via either an unsupervised learning approach called "learning by confusion" or a widely used supervised learning approach. We find that after the training via either the unsupervised learning approach or the supervised learning one, all of these three different representative types of NNs, namely, the fully-connected NN, the convolutional NN, and the residual NN, are able to successfully identify the first-order flocking transition point of this nonequilibrium many-body system. This indicates that NN based machine learning can be employed as a promising generic tool to investigate rich physics in scenarios associated to first-order phase transitions and nonequilibrium many-body systems.Comment: 7 pages, 3 figure

The subordinated processes controlled by a family of subordinators and corresponding Fokker-Planck type equations

In this work, we consider subordinated processes controlled by a family of subordinators which consist of a power function of time variable and a negative power function of $\alpha-$stable random variable. The effect of parameters in the subordinators on the subordinated process is discussed. By suitable variable substitutions and Laplace transform technique, the corresponding fractional Fokker-Planck-type equations are derived. We also compute their mean square displacements in a free force field. By choosing suitable ranges of parameters, the resulting subordinated processes may be subdiffusive, normal diffusive or superdiffusive.Comment: 11 pages, accepted by J. Stat. Mech.: Theor. Ex