Layer Antiferromagnetic State in Bilayer Graphene : A First-Principle Investigation

The ground state of bilayer graphene is investigated by the density functional calculations with local spin density approximation. We find a ground state with layer antiferromagnetic ordering, which has been suggested by former studies based on simplified model. The calculations prove that the layer antiferromagnetic state (LAF) is stable even if the remote hopping and nonlocal Coulomb interaction are included. The gap of the LAF state is about 1.8 meV, comparable to the experimental value. The surface magnetism in BLG is of the order of $10^{-2} \mu_B /nm^2$

Topological phase transition and nontrivial thermal Hall signatures in honeycomb lattice magnets

We investigate spinon band topology and engineering from the interplay between long-ranged magnetic order and fractionalized spinons, as well as Zeeman coupling under external magnetic fields, in honeycomb lattice magnets. The synergism of N\'eel order and magnetic fields could reconstruct the spinon bands and drive a topological phase transition from the coexisting phase of long-ranged order and chiral spin liquid with semion topological order to the conventional magnetic order. Our prediction can be immediately tested through thermal Hall transport measurements among the honeycomb lattice magnets that are tuned to be proximate to the quantum critical point. Our theory should also shed light on the critical behavior of honeycomb Kitaev materials with emergent Majorana fermion bands. We suggest a possible relevance to the spin-1/2 honeycomb spin liquid candidate material In$_3$Cu$_2$VO$_9$.Comment: 6 figures, may submit to a domestic journal of China, paper explanation is found https://gangchengroup-physics.weebly.com/paper-explanation.htm

Carbon monoxide in an extremely metal-poor galaxy

Extremely metal-poor galaxies with metallicity below 10% of the solar value in the local universe are the best analogues to investigating the interstellar medium at a quasi-primitive environment in the early universe. In spite of the ongoing formation of stars in these galaxies, the presence of molecular gas (which is known to provide the material reservoir for star formation in galaxies, such as our Milky Way) remains unclear. Here, we report the detection of carbon monoxide (CO), the primary tracer of molecular gas, in a galaxy with 7% solar metallicity, with additional detections in two galaxies at higher metallicities. Such detections offer direct evidence for the existence of molecular gas in these galaxies that contain few metals. Using archived infrared data, it is shown that the molecular gas mass per CO luminosity at extremely low metallicity is approximately one-thousand times the Milky Way value.Comment: 12 pages, 3 figures, 1 table. Supplementary data at http://www.nature.com/article-assets/npg/ncomms/2016/161209/ncomms13789/extref/ncomms13789-s1.pd

Multi-Agent Game Abstraction via Graph Attention Neural Network

In large-scale multi-agent systems, the large number of agents and complex game relationship cause great difficulty for policy learning. Therefore, simplifying the learning process is an important research issue. In many multi-agent systems, the interactions between agents often happen locally, which means that agents neither need to coordinate with all other agents nor need to coordinate with others all the time. Traditional methods attempt to use pre-defined rules to capture the interaction relationship between agents. However, the methods cannot be directly used in a large-scale environment due to the difficulty of transforming the complex interactions between agents into rules. In this paper, we model the relationship between agents by a complete graph and propose a novel game abstraction mechanism based on two-stage attention network (G2ANet), which can indicate whether there is an interaction between two agents and the importance of the interaction. We integrate this detection mechanism into graph neural network-based multi-agent reinforcement learning for conducting game abstraction and propose two novel learning algorithms GA-Comm and GA-AC. We conduct experiments in Traffic Junction and Predator-Prey. The results indicate that the proposed methods can simplify the learning process and meanwhile get better asymptotic performance compared with state-of-the-art algorithms.Comment: Accepted by AAAI202