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Application of Machine Training Methods to Design of New Inorganic Compounds
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Author(s): Nadezhda Kiselyova (A. A. Baikov Institute of Metallurgy and Materials Science of Russian Academy of Sciences, Russia), Andrey Stolyarenko (A. A. Baikov Institute of Metallurgy and Materials Science of Russian Academy of Sciences, Russia), Vladimir Ryazanov (A. A. Dorodnicyn Computing Centre of Russian Academy of Sciences, Russia), Oleg Sen’ko (Oleg Sen’koA. A. Dorodnicyn Computing Centre of Russian Academy of Sciences, Russia)and Alexandr Dokukin (A. A. Dorodnicyn Computing Centre of Russian Academy of Sciences, Russia)
Copyright: 2013
Pages: 24
Source title:
Diagnostic Test Approaches to Machine Learning and Commonsense Reasoning Systems
Source Author(s)/Editor(s): Xenia Naidenova (Military Medical Academy, Russia)and Dmitry I. Ignatov (National Research University Higher School of Economics, Russia)
DOI: 10.4018/978-1-4666-1900-5.ch009
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Abstract
The review of applications of machine training methods to inorganic chemistry and materials science is presented. The possibility of searching for classification regularities in large arrays of chemical information with the use precedent-based recognition methods is discussed. The system for computer-assisted design of inorganic compounds, with an integrated complex of databases for the properties of inorganic substances and materials, a subsystem for the analysis of data, based on computer training (including symbolic pattern recognition methods), a knowledge base, a predictions base, and a managing subsystem, has been developed. In many instances, the employment of the developed system makes it possible to predict new inorganic compounds and estimate various properties of those without experimental synthesis. The results of application of this information-analytical system to the computer-assisted design of inorganic compounds promising for the search for new materials for electronics are presented.
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