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Machine Learning Application to Predict New Inorganic Compounds – Results and Perspectives

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Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1620))

Abstract

A brief overview of the problems is given in the field of inorganic chemistry and materials science, solved using machine learning (ML). The main ML methods limitations and the subject area peculiarities are considered that must be taken into account when using ML. Solved problems examples of new inorganic compounds design and the results of comparing predictions with new experimental data are given. Systems developed by the authors are considered that aimed at not yet obtained inorganic compounds design, based on ML methods, as well as promising directions for such systems development in order to improve the predictions accuracy for new substances and their corresponding properties values estimations.

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Acknowledgements

The authors are grateful to V.V. Ryazanov, O. V. Sen’ko, A.A. Dokukin, V.S. Pereverzev-Orlov, M.A. Vitushko, and E.A. Vaschenko for their help in developing algorithms and programs. This work was supported in part by the Russian Foundation for Basic Research, project nos. 20-01-00609 and 18-07-00080. The study was carried out as part of the state assignment (project no. № 075-00328-21-00).

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Authors and Affiliations

  1. A. A. Baikov Institute of Metallurgy and Materials Science of RAS (IMET RAS), Moscow, 119334, Russia

    Nadezhda Kiselyova, Victor Dudarev & Andrey Stolyarenko

  2. HSE University, Moscow, 109028, Russia

    Victor Dudarev

Authors
  1. Nadezhda Kiselyova
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  2. Victor Dudarev
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  3. Andrey Stolyarenko
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Corresponding author

Correspondence to Victor Dudarev .

Editor information

Editors and Affiliations

  1. Space Research Institute of the Russian Academy of Sciences, Moscow, Russia

    Alexei Pozanenko

  2. Federal Research Center “Computer Science and Control” of RAS, Moscow, Russia

    Sergey Stupnikov

  3. Christian-Albrecht University of Kiel, Kiel, Germany

    Bernhard Thalheim

  4. Universidad Carlos III de Madrid, Getafe, Spain

    Eva Mendez

  5. A. A. Baikov Institute of Metallurgy and Materials Science of RAS (IMET RAS), Moscow, Russia

    Nadezhda Kiselyova

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Kiselyova, N., Dudarev, V., Stolyarenko, A. (2022). Machine Learning Application to Predict New Inorganic Compounds – Results and Perspectives. In: Pozanenko, A., Stupnikov, S., Thalheim, B., Mendez, E., Kiselyova, N. (eds) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2021. Communications in Computer and Information Science, vol 1620. Springer, Cham. https://doi.org/10.1007/978-3-031-12285-9_9

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  • DOI: https://doi.org/10.1007/978-3-031-12285-9_9

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  • Online ISBN: 978-3-031-12285-9

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