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A fair high TC of 303 K is predicted for Mo2C-FO. Our DFT+U calculation results also provide a mechanism of magnetoelectric coupling, in which the reversal of electric polarization is driven by terminal-layer atom-pair flipping.

Magnetoelectric annealing is necessary to remove antiferromagnetic domains and induce macroscopic magnetoelectric effect in polycrystalline magnetoelectric materials, and in this paper, we study the effective magnetoelectric properties of perpendicularly annealed polycrystalline Cr 2 O 3 using effective medium approximation. The effect of temperatures, grain aspect ratios, and two different types of orientation distribution function have been analyzed, and unusual material symmetry is observed when the orientation distribution function only depends on Euler angle . Optimal grain aspect ratio and texture coefficient are also identified. The approach can be applied to analyze the microstructural field distribution and macroscopic properties of a wide range of magnetoelectric polycrystals.

Nonlinear optical crystals have been a key material in lasers due to their excellent frequency conversion properties. The search for new nonlinear crystals has been extremely active due to the inherent drawbacks of existing nonlinear crystals, such as the low laser damage threshold of infrared nonlinear crystals and the phenomenon of two‐photon absorption. The stability of a material can be characterized by calculating the formation energy, and machine learning (ML) is currently the most mainstream tool to explore materials. In this paper, the digital features of nonlinear crystals are established by using only the component information. Based on the low‐variance and cross‐validated recursive feature elimination (RFECV), 11 features with strong correlation are obtained for model training. Among them, the two features related to electronegativity account for 71.44% of all features, and become the most critical factor for the formation energy predicted by the model. Three ML regression models have been proposed to predict the formation energy of nonlinear crystals. Among them, the gradient boosting regression model shows excellent prediction performance with R2 = 0.939, RMSE = 0.205, and MAE = 0.132 eV per atom. The model provides a useful tool for fast and low‐cost prediction of nonlinear crystal formation energy.

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