Liquid–Crystal Structure Inheritance in Machine Learning Potentials for Network-Forming Systems
Электронный научный архив УРФУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
| Заглавие |
Liquid–Crystal Structure Inheritance in Machine Learning Potentials for Network-Forming Systems
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| Автор |
Balyakin, I. A.
Ryltsev, R. E. Chtchelkatchev, N. M. |
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| Описание |
It has been studied whether machine learning interatomic potentials parameterized with only disordered configurations corresponding to liquid can describe the properties of crystalline phases and predict their structure. The study has been performed for a network-forming system SiO2, which has numerous polymorphic phases significantly different in structure and density. Using only high-temperature disordered configurations, a machine learning interatomic potential based on artificial neural networks (DeePMD model) has been parameterized. The potential reproduces well ab initio dependences of the energy on the volume and the vibrational density of states for all considered tetra- and octahedral crystalline phases of SiO2. Furthermore, the combination of the evolutionary algorithm and the developed DeePMD potential has made it possible to reproduce the really observed crystalline structures of SiO2. Such a good liquid–crystal portability of the machine learning interatomic potential opens prospects for the simulation of the structure and properties of new systems for which experimental information on crystalline phases is absent. © 2023, The Author(s).
Russian Academy of Sciences, РАН; Ural Branch, Russian Academy of Sciences, UB RAS; Russian Science Foundation, RSF: 22-22-00506; National Research Center "Kurchatov Institute", NRC KI This study was supported by the Russian Science Foundation (project no. 22-22-00506, https://rscf.ru/project/22-22-00506/ ). The numerical calculations were carried out using the Uran supercomputer, Institute of Mathematics and Mechanics, Ural Branch, Russian Academy of Sciences; equipment of the Common Access Center Complex for Modeling and Processing of Data of Mega-Class Research Facilities, National Research Center Kurchatov Institute (http://ckp.nrcki.ru/); and computational resources of the Interdisciplinary Computer Center, Russian Academy of Sciences. |
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| Дата |
2024-04-05T16:20:20Z
2024-04-05T16:20:20Z 2023 |
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| Тип |
Article
Journal article (info:eu-repo/semantics/article) |info:eu-repo/semantics/publishedVersion |
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| Идентификатор |
Balyakin, IA, Ryltsev, RE & Chtchelkatchev, NM 2023, 'Liquid–Crystal Structure Inheritance in Machine Learning Potentials for Network-Forming Systems', JETP Letters, Том. 117, № 5, стр. 370-376. https://doi.org/10.1134/S0021364023600234
Balyakin, I. A., Ryltsev, R. E., & Chtchelkatchev, N. M. (2023). Liquid–Crystal Structure Inheritance in Machine Learning Potentials for Network-Forming Systems. JETP Letters, 117(5), 370-376. https://doi.org/10.1134/S0021364023600234 0021-3640 Final All Open Access, Hybrid Gold https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153873071&doi=10.1134%2fS0021364023600234&partnerID=40&md5=0f788a018c768ee9765f2a33f8753a3d https://link.springer.com/content/pdf/10.1134/S0021364023600234.pdf http://elar.urfu.ru/handle/10995/130427 10.1134/S0021364023600234 85153873071 000975208100009 |
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| Язык |
en
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| Связанные ресурсы |
info:eu-repo/grantAgreement/RSF//22-22-00506
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| Права |
Open access (info:eu-repo/semantics/openAccess)
cc-by https://creativecommons.org/licenses/by/4.0/ |
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| Формат |
application/pdf
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| Издатель |
Pleiades Publishing
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| Источник |
JETP Letters
JETP Letters |
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