The interplay of magnetism and topology is a key research subject in condensed matter physics, which offers great opportunities to explore emerging new physics, such as the quantum anomalous Hall (QAH) effect, axion electrodynamics, and Majorana fermions. However, these exotic physical effects have rarely been realized experimentally because of the lack of suitable working materials. Here, we predict a series of van der Waals layered MnBi2Te4-related materials that show intralayer ferromagnetic and interlayer antiferromagnetic exchange interactions. We find extremely rich topological quantum states with outstanding characteristics in MnBi2Te4, including an antiferromagnetic topological insulator with the long-sought topological axion states on the surface, a type II magnetic Weyl semimetal with one pair of Weyl points, as well as a collection of intrinsic axion insulators and QAH insulators in even- and odd-layer films, respectively. These notable predictions, if proven experimentally, could profoundly change future research and technology of topological quantum physics.

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Hydrogen exhibits qualitatively different charge states depending on the host material, as nicely explained by the state-of-the-art impurity-state calculation. Motivated by a recent experiment [Nature 546, 124 (2017)], we show that the complex oxide SrCoO2.5 represents an interesting example, in which the interstitial H appears as a deep-level center according to the commonly-used transition level calculation, but no bound electron can be found around the impurity. Via a combination of charge difference analysis, density of states projection and constraint magnetization calculation, it turns out that the H-doped electron is spontaneously trapped by a nonunique Co ion and is fully spin-polarized by the onsite Hund's rule coupling. Consequently, the doped system remains insulating, whereas the antiferromagnetic exchange is slightly perturbed locally.

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A recently discovered high-Tc cuprate superconductor Ba2CuO4-δ exhibits exceptional Jahn-Teller distortion, wherein the CuO6 octahedrons are compressed along the c axis. As a consequence, the O vacancies prefer to reside in the CuO2 plane, but the exact structure is not known. By combining first-principles total energy calculation with the automated structure inversion method, the effective cluster interactions of O vacancies are mapped out. Around δ=0.8, where the 73K superconductivity was observed experimentally, we predict that the ordered O vacancies slice the CuO2 plane into not only 1D chains and but also two-leg ladders. A Monte Carlo simulation is performed based on the effective cluster interaction model, showing that such an ordering pattern is stable up to ~900 K. Our results put forth a concrete structural basis to discuss the underlying superconducting mechanism.2 Main Text:High-Tc superconductivity in cuprates is commonly spawned in the intact two-dimensional CuO2 plane [1][2][3][4][5][6]. However, a new cuprate superconductor Ba2CuO4-δ appears to be an exception [7]. Ba2CuO4-δ crystallizes into the typical 214 layered perovskite structure [ Fig. 1(a)], but the CuO6 octahedrons are largely compressed along the c axis, making the in-plane Cu-O bonds weaker than the out-of-plane ones. Consequently, in contrast to the apical substitution as typically observed in other high-Tc cuprates [3][4][5][6], here the O vacancies prefer to be created in the CuO2 plane [8,9]. Surprisingly, superconductivity emerges at a very high concentration of in-plane O vacancies, when the 2D parent lattice has been strongly disrupted, and the superconducting transition temperature reaches as high as 73 K around δ=0.8.At present, the in-plane O vacancy structure is still largely unknown except for the δ=1 limit, i.e. Ba2CuO3 [8]. This stoichiometric compound as a quasi-1D Mott insulator consists of paralleled (-O-Cu-O-) chains. One can reversely consider that superconductivity emerges from Ba2CuO3+γ around γ=0.2, when excess O atoms link the 1D chains, which appears to play an important role in the 73 K superconductivity, because in other quasi-1D cuprates with charge doping only, such a high Tc has never been observed.[9-12] An interesting observation is that the atomic structure of Ba2CuO3 can be viewed as long-range ordering of in-plane O vacancies in Ba2CuO4. Ba2CuO3 shares exactly the same lattice as Ba2CuO4, with only slight changes of the lattice constants (Tab. I). The O atoms are missing along all the unidirectional (-O-Cu-O-) arrays, like cutting either the warp or the weft of a fabric. The coordinates of the remaining atoms stay almost unchanged.

By performing first-principles calculations, we find that the predominant spin exchange of a hexagonal CoO2 layer is in proximity to the ferromagnetic-to-antiferromagnetic transition point. Its magnetic ground state can be easily altered by, e.g., substrate dielectricity and strain. In addition, the dopability of a stack of CoO2 layers is found to sensitively depend on the interlayer distance, which not only renders effective manipulation of the electronic property but also reveals an important intercalation effect in related bulk materials.

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Molecular dynamics is a powerful simulation tool to explore material properties. Most realistic material systems are too large to be simulated using first-principles molecular dynamics. Classical molecular dynamics has a lower computational cost but requires accurate force fields to achieve chemical accuracy. In this work, we develop a symmetry-adapted graph neural network framework called the molecular dynamics graph neural network (MDGNN) to construct force fields automatically for molecular dynamics simulations for both molecules and crystals. This architecture consistently preserves translation, rotation, and permutation invariance in the simulations. We also propose a new feature engineering method that includes high-order terms of interatomic distances and demonstrate that the MDGNN accurately reproduces the results of both classical and first-principles molecular dynamics. In addition, we demonstrate that force fields constructed by the proposed model have good transferability. The MDGNN is thus an efficient and promising option for performing molecular dynamics simulations of large-scale systems with high accuracy.