Prediction of the space group of perovskite-like compounds of the composition AII2BIIIB′VO6

The prediction of new compounds of the composition AII2BIIIB′VO6 was carried out, the type of distortion of their perovskite-like lattice, the space group were predicted, and the parameters of the crystal lattice of the predicted compounds were estimated. When predicting, only the property values of the chemical elements were used. Programs based on machine learning algorithms for various variants of neural networks, a linear machine, the formation of logical regularities, k-nearest neighbors, support vector machine showed the best results when predicting the type of distortion of a perovskite-like lattice. When evaluating the lattice parameters, the programs based on algorithms for orthogonal matching pursuit and automatic relevance determination regression were the most accurate methods. The accuracy of predictions of the perovskite-like lattice distortion type was no less than 74 %. The accuracy of estimating the lattice linear parameters was within ± 0.0120 – 0.8264 Å, and the accuracy for angles β with monoclinic distortion of the lattice was ± 0.08 – 0.74 deg. The calculations were carried out using systems based on machine learning methods. To evaluate the prediction accuracy, an exam recognition in the cross-validation mode was used for the compounds included in the sample for machine learning. The predicted compounds are promising for the search for new magnetic, thermoelectric and dielectric materials.