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Computer Modeling and Machine Learning in Chemistry and Materials Science: From Properties and Reactions of Small Organic and Inorganic Molecules to the Smart Design of Polymers and Composites. / Novikov, Alexander S.

в: Compounds, Том 3, № 3, 24.08.2023, стр. 459-463.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

Harvard

Novikov, AS 2023, 'Computer Modeling and Machine Learning in Chemistry and Materials Science: From Properties and Reactions of Small Organic and Inorganic Molecules to the Smart Design of Polymers and Composites', Compounds, Том. 3, № 3, стр. 459-463. https://doi.org/10.3390/compounds3030034

APA

Novikov, A. S. (2023). Computer Modeling and Machine Learning in Chemistry and Materials Science: From Properties and Reactions of Small Organic and Inorganic Molecules to the Smart Design of Polymers and Composites. Compounds, 3(3), 459-463. https://doi.org/10.3390/compounds3030034

Vancouver

Novikov AS. Computer Modeling and Machine Learning in Chemistry and Materials Science: From Properties and Reactions of Small Organic and Inorganic Molecules to the Smart Design of Polymers and Composites. Compounds. 2023 Авг. 24;3(3):459-463. https://doi.org/10.3390/compounds3030034

Author

Novikov, Alexander S. / Computer Modeling and Machine Learning in Chemistry and Materials Science: From Properties and Reactions of Small Organic and Inorganic Molecules to the Smart Design of Polymers and Composites. в: Compounds. 2023 ; Том 3, № 3. стр. 459-463.

BibTeX

@article{d4a3b88d662944d3b7936b79c1305cc7,
title = "Computer Modeling and Machine Learning in Chemistry and Materials Science: From Properties and Reactions of Small Organic and Inorganic Molecules to the Smart Design of Polymers and Composites",
abstract = "Computer modeling, machine learning, and artificial intelligence are currently considered cutting-edge topics in chemistry and materials science. The application of information technologies in natural sciences can help researchers collect big data and understand patterns that are not obvious to humans. In this perspective, I would like to highlight the recent achievements of our research group and other researchers in relation to computer modeling and machine learning in chemistry and materials science.",
author = "Novikov, {Alexander S.}",
year = "2023",
month = aug,
day = "24",
doi = "10.3390/compounds3030034",
language = "English",
volume = "3",
pages = "459--463",
journal = "Compounds",
issn = "2673-6918",
publisher = "MDPI AG",
number = "3",

}

RIS

TY - JOUR

T1 - Computer Modeling and Machine Learning in Chemistry and Materials Science: From Properties and Reactions of Small Organic and Inorganic Molecules to the Smart Design of Polymers and Composites

AU - Novikov, Alexander S.

PY - 2023/8/24

Y1 - 2023/8/24

N2 - Computer modeling, machine learning, and artificial intelligence are currently considered cutting-edge topics in chemistry and materials science. The application of information technologies in natural sciences can help researchers collect big data and understand patterns that are not obvious to humans. In this perspective, I would like to highlight the recent achievements of our research group and other researchers in relation to computer modeling and machine learning in chemistry and materials science.

AB - Computer modeling, machine learning, and artificial intelligence are currently considered cutting-edge topics in chemistry and materials science. The application of information technologies in natural sciences can help researchers collect big data and understand patterns that are not obvious to humans. In this perspective, I would like to highlight the recent achievements of our research group and other researchers in relation to computer modeling and machine learning in chemistry and materials science.

UR - https://www.mendeley.com/catalogue/de12f461-6641-30f5-9c52-29641e22fbd4/

U2 - 10.3390/compounds3030034

DO - 10.3390/compounds3030034

M3 - Article

VL - 3

SP - 459

EP - 463

JO - Compounds

JF - Compounds

SN - 2673-6918

IS - 3

ER -

ID: 110716486