gg-wave Pairing in BiS2_2 Superconductors

Recent angle resolved photoemission spectroscopy(ARPES) experiments have suggested that BiS$_2$ based superconductors are at very low electron doping. Using random phase approximation(RPA) and functional renormalization group(FRG) methods, we find that $g$-wave pairing symmetry belonging to A$_{2g}$ irreducible representation is dominant at electron doping $x<0.25$. The pairing symmetry is determined by inter-pocket nesting and orbital characters on the Fermi surfaces and is robust in a two-orbital model including both Hund's coupling $J$, and Hubbard-like Coulomb interactions $U$ and $U'$ with relatively small $J$ ($J\leq0.2U$). With the increasing electron doping, the g-wave state competes with both the s-wave $A_{1g}$ and d-wave $B_{2g}$ states and no pairing symmetry emerges dominantly.Comment: published version, EPL(editor's choice

Quantum coherence of the molecular states and their corresponding currents in nanoscale Aharonov-Bohm interferometers

By considering a nanoscale Aharonov-Bohm (AB) interferometer containing a parrallel-coupled double dot coupled to the source and drain electrodes, we investigate the AB phase oscillations of transport current via the bonding and antibonding state channels. The results we obtained justify the experimental analysis given in [Phys. Rev. Lett. \textbf{106}, 076801 (2011)] that bonding state currents in different energy configurations are almost the same. On the other hand, we extend the analysis to the transient transport current components flowing through different channels, to explore the effect of the parity of bonding and antibonding states on the AB phase dependence of the corresponding current components in the transient regime. The relations of the AB phase dependence between the quantum states and the associated current components are analyzed in details, which provides useful information for the reconstruction of quantum states through the measurement of the transport current in such systems. With the coherent properties in the quantum dot states as well as in the transport currents, we also provide a way to manipulate the bonding and antibonding states by the AB magnetic flux.Comment: 10 pages, 7 figure

A Mass-Conserving-Perceptron for Machine Learning-Based Modeling of Geoscientific Systems

Although decades of effort have been devoted to building Physical-Conceptual (PC) models for predicting the time-series evolution of geoscientific systems, recent work shows that Machine Learning (ML) based Gated Recurrent Neural Network technology can be used to develop models that are much more accurate. However, the difficulty of extracting physical understanding from ML-based models complicates their utility for enhancing scientific knowledge regarding system structure and function. Here, we propose a physically-interpretable Mass Conserving Perceptron (MCP) as a way to bridge the gap between PC-based and ML-based modeling approaches. The MCP exploits the inherent isomorphism between the directed graph structures underlying both PC models and GRNNs to explicitly represent the mass-conserving nature of physical processes while enabling the functional nature of such processes to be directly learned (in an interpretable manner) from available data using off-the-shelf ML technology. As a proof of concept, we investigate the functional expressivity (capacity) of the MCP, explore its ability to parsimoniously represent the rainfall-runoff (RR) dynamics of the Leaf River Basin, and demonstrate its utility for scientific hypothesis testing. To conclude, we discuss extensions of the concept to enable ML-based physical-conceptual representation of the coupled nature of mass-energy-information flows through geoscientific systems.Comment: 60 pages and 7 figures in the main text. 10 figures, and 10 tables in the supplementary material