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Motivated by the recent discovery of charge density wave (CDW) order in the magnetic kagom\'e metal FeGe, we study the single-orbital $t$-$U$-$V_1$-$V_2$ model on the kagom\'e lattice, where $U$, $V_1$, and $V_2$ are the onsite, nearest neighbor, and next-nearest-neighbor Coulomb repulsions, respectively. When the Fermi level lies in the flat band, the instability toward ferromagnetic (FM) order gives rise to a FM half-metal at sufficiently large onsite $U$. Intriguingly, at band filling $n=17/24$, the Fermi level crosses the van Hove singularity of the spin-minority bands of the half-metal. We show that, due to the unique geometry and sublattice interference on the kagom\'e lattice at van Hove singularity, the intersite Coulomb interactions $V_1$ and $V_2$ drive a real and an imaginary bond-ordered $2a_0 \times 2a_0$ CDW instability, respectively. The FM loop current CDW with complex bond orders is a spin-polarized Chern insulator exhibiting the quantum anomalous Hall effect. The bond fluctuations are found to be substantially enhanced compared to the corresponding nonmagnetic kagom\'e metals at van Hove filling, providing a concrete model realization of the bond-ordered CDWs, including the FM loop current CDW, over the onsite charge density ordered states. When the spins are partially polarized, we find that the formation of bond-ordered CDWs enhances substantially the ordered magnetic moments. These findings provide physical insights for the emergence of loop-current and bond-ordered CDW and their interplay with magnetism on the kagom\'e lattice, with possible connections to the magnetic kagom\'e metal FeGe.

The theory of equilibrium phase transition, which traditionally does not involve time evolution, concerns only static quantities. In recent decades, owing to the development of nonequilibrium thermodynamics, there is a growing interest in nonequilibrium phase transition which concerns time evolution. It is well-known that in the absence of phase transitions, the excess work resulted from finite-rate quench is proportional to the quench rate v. The time evolution in a second-order phase transition can be understood via the Kibble-Zurek mechanism, and the scaling of work is related to the critical exponents. Nevertheless, the finite-time nonequilibrium thermodynamics of the first-order phase transition remains largely unexplored. We investigate the scaling relations of some thermodynamic quantities with the quench rate in the first-order phase transition. It is shown that the excess work scales as $v^{2/3}$ for small quench rate. Moreover, the delay time and the transition time scale as $v^{1/3}$. Our study deepens the understanding about the nonequilibrium thermodynamics of the first-order phase transition.

The stochastic thermodynamics of systems with a few degrees of freedom has been studied extensively so far. We would like to extend the study to systems with more degrees of freedom and even further-continuous fields with infinite degrees of freedom. The simplest case for a continuous stochastic field is the Edwards-Wilkinson elastic manifold. It is an exactly solvable model of which the heat statistics in the relaxation process can be calculated analytically. The cumulants require a cutoff spacing to avoid ultra-violet divergence. The scaling behavior of the heat cumulants with time and the system size as well as the large deviation rate function of the heat statistics in the large size limit is obtained.

We provide a detailed investigation on the fluctuations of the currents in the discrete model of Feynman's ratchet proposed by Jarzynski and Mazonka in 1999. Two macroscopic currents are identified, with the corresponding affinities determined using Schnakenberg's graph analysis. We also investigate full counting statistics of the two currents and show that fluctuation theorem holds for their joint probability distribution. Moreover, fluctuation-dissipation relation, Onsager reciprocal relation and their nonlinear generalizations are numerically shown to be satisfied in this model.

Precise vertex reconstruction is essential for large liquid scintillator detectors. A novel method based on machine learning has been successfully developed to reconstruct the event vertex in JUNO previously. In this paper, the performance of machine learning based vertex reconstruction is further improved by optimizing the input images of the neural networks. By separating the information of different types of PMTs as well as adding the information of the second hit of PMTs, the vertex resolution is improved by about 9.4 % at 1 MeV and 9.8 % at 11 MeV, respectively.

Intrinsic interpretability of graph neural networks (GNNs) is to find a small subset of the input graph's features -- rationale -- which guides the model prediction. Unfortunately, the leading rationalization models often rely on data biases, especially shortcut features, to compose rationales and make predictions without probing the critical and causal patterns. Moreover, such data biases easily change outside the training distribution. As a result, these models suffer from a huge drop in interpretability and predictive performance on out-of-distribution data. In this work, we propose a new strategy of discovering invariant rationale (DIR) to construct intrinsically interpretable GNNs. It conducts interventions on the training distribution to create multiple interventional distributions. Then it approaches the causal rationales that are invariant across different distributions while filtering out the spurious patterns that are unstable. Experiments on both synthetic and real-world datasets validate the superiority of our DIR in terms of interpretability and generalization ability on graph classification over the leading baselines. Code and datasets are available at https://github.com/Wuyxin/DIR-GNN., Comment: ICLR 2022

We study the pairing symmetry and the topological properties of the hydrogen-doped ACr$_3$As$_3$ superconductors. Based on our first-principle band structure with spin-orbit-coupling (SOC), we construct tight binding model including the on-site SOC terms, equipped with the multi-orbital Hubbard interactions. Then using the random-phase-approximation (RPA) approach, we calculate the pairing phase diagram of this model. Our RPA results yield the triplet pz-wave pairing in the ($\left(\uparrow\downarrow+\downarrow\uparrow\right)$) spin channel to be the leading pairing symmetry all over the experiment relevant hydrogen-doping regimes. This pairing state belongs to the spin-U(1)-symmetry protected time-reversal-invariant topological nodal-line superconductivity. Determined by the momentum-dependent topological invariant Z (kx; ky), the whole (001) surface Brillouin zone is covered with topological flat bands with different regimes covered with different numbers of flat surface bands, which can be detected by the scanning tunneling microscope experiments., Comment: 7 pages, 6 figures

Explainability of graph neural networks (GNNs) aims to answer "Why the GNN made a certain prediction?", which is crucial to interpret the model prediction. The feature attribution framework distributes a GNN's prediction to its input features (e.g., edges), identifying an influential subgraph as the explanation. When evaluating the explanation (i.e., subgraph importance), a standard way is to audit the model prediction based on the subgraph solely. However, we argue that a distribution shift exists between the full graph and the subgraph, causing the out-of-distribution problem. Furthermore, with an in-depth causal analysis, we find the OOD effect acts as the confounder, which brings spurious associations between the subgraph importance and model prediction, making the evaluation less reliable. In this work, we propose Deconfounded Subgraph Evaluation (DSE) which assesses the causal effect of an explanatory subgraph on the model prediction. While the distribution shift is generally intractable, we employ the front-door adjustment and introduce a surrogate variable of the subgraphs. Specifically, we devise a generative model to generate the plausible surrogates that conform to the data distribution, thus approaching the unbiased estimation of subgraph importance. Empirical results demonstrate the effectiveness of DSE in terms of explanation fidelity., Comment: 18 pages