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Predictions of Chalcospinels with Composition ABCX4 (X = S or Se)

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Inorganic Materials: Applied Research Aims and scope

Abstract

New chalcospinels of the most common compositions were predicted: AIBIIICIVX4 (X = S or Se) and AIIBIIICIIIS4 (A, B, and C are various chemical elements). They are promising for the search for new materials for magneto-optical memory elements, sensors, and anodes in sodium-ion batteries. The parameter “a” values of their crystal lattice are estimated. When predicting, only the values of the properties of chemical elements were used. The calculations were carried out using machine learning programs that are part of the information-analytical system developed by the authors (various ensembles of algorithms of the binary decision trees, the linear machine, the search for logical regularities of classes, the support vector machine, Fisher linear discriminant, the k-nearest neighbors, the learning a multilayer perceptron, and a neural network) for predicting chalcospinels not yet obtained, as well as an extensive family of regression methods, presented in the scikit-learn package for the Python language, and multilevel machine learning methods that were proposed by the authors for estimation of the lattice parameter value of new chalcospinels. The prediction accuracy of new chalcospinels according to the results of the cross-validation is not lower than 80%, and the prediction accuracy of the parameter of their crystal lattice (according to the results of calculating the mean absolute error when cross-validation in the leave-one-out mode) is ±0.1 Å. The effectiveness of using multilevel machine learning methods to predict the physical properties of substances is shown.

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Funding

This study was partially supported by the Russian Foundation for Basic Research, projects 20-01-00609 and 18-07-00080. The study was carried out in accordance with state assignment nos. 007-00129-18-00 and 0063-2020-0003.

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Authors and Affiliations

  1. Baikov Institute of Metallurgy and Materials Science of Russian Academy of Sciences, 119334, Moscow, Russia

    N. N. Kiselyova, V. A. Dudarev & A. A. Dokukin

  2. Higher School of Economics (National Research University), 101000, Moscow, Russia

    V. A. Dudarev

  3. Federal Research Center Computer Science and Control, Russian Academy of Sciences, 119333, Moscow, Russia

    V. V. Ryazanov, O. V. Sen’ko & A. A. Dokukin

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  1. N. N. Kiselyova
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  2. V. A. Dudarev
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  3. V. V. Ryazanov
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  4. O. V. Sen’ko
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Correspondence to N. N. Kiselyova.

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Translated by K. Gumerov

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Kiselyova, N.N., Dudarev, V.A., Ryazanov, V.V. et al. Predictions of Chalcospinels with Composition ABCX4 (X = S or Se). Inorg. Mater. Appl. Res. 12, 328–336 (2021). https://doi.org/10.1134/S2075113321020246

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