Nitrides are of increasing interest since they are usually wide-bandgap semiconductors and the available environmental raw materials are abundant. Using first-principles predictions, we reveal that Mg2XN3 (X = V, Cr) compounds show remarkably large ferroelectric polarization and piezoelectric response. Quantitative theoretical analysis further indicates the asymmetric orbital hybridization to be the origin of the ferroelectricity. Since Cr has one more 3d electron than V, it is found that Mg2CrN3 is multiferroic with a ferromagnetic ground state. We further show that the epitaxial strain can regulate the piezoelectricity, and thus, both Mg2CrN3 and Mg2VN3 exhibit a larger piezoelectric response than the reported nitride piezoelectric materials under appropriate tensile epitaxial strain. Our findings provide guidance for potential applications of nitride materials in spintronics, sensors, and memory devices.

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Machine learning (ML) accelerates the rational design and discovery of materials, where the feature plays a critical role in the ML model training. We propose a low-cost electron probability waves (EPW) descriptor based on electronic structures, which is extracted from highsymmetry points in the Brillouin zone. In the task of distinguishing ferromagnetic or antiferromagnetic material, it achieves an accuracy (ACC) at 0.92 and an area under the receiver operating characteristic curve (AUC) at 0.83 by 10-fold cross-validation. Furthermore, EPW excels at classifying metal/semiconductors and judging the direct/indirect bandgap of semiconductors. The distribution of electron clouds is an essential criterion for the origin of ferromagnetism, and EPW acts as an emulation of the electronic structure, which is the key to the achievements. Our EPW-based ML model obtains ACC and AUC equivalent to crystal graph features-based deep learning models for tasks with physical recognitions in electronic states.