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Machine Learning As a Tool to Accelerate the Search for New Materials for Metal-Ion Batteries. / Osipov, V. T.; Gongola, M. I.; Morkhova, Ye A. et al.

In: Doklady Mathematics, Vol. 108, No. Suppl 2, 12.2023, p. S476-S483.

Research output: Contribution to journal › Article › peer-review

Harvard

Osipov, VT, Gongola, MI, Morkhova, YA, Nemudryi, AP & Kabanov, AA 2023, 'Machine Learning As a Tool to Accelerate the Search for New Materials for Metal-Ion Batteries', Doklady Mathematics, vol. 108, no. Suppl 2, pp. S476-S483. https://doi.org/10.1134/S1064562423701612

APA

Osipov, V. T., Gongola, M. I., Morkhova, Y. A., Nemudryi, A. P., & Kabanov, A. A. (2023). Machine Learning As a Tool to Accelerate the Search for New Materials for Metal-Ion Batteries. Doklady Mathematics, 108(Suppl 2), S476-S483. https://doi.org/10.1134/S1064562423701612

Vancouver

Osipov VT, Gongola MI, Morkhova YA, Nemudryi AP, Kabanov AA. Machine Learning As a Tool to Accelerate the Search for New Materials for Metal-Ion Batteries. Doklady Mathematics. 2023 Dec;108(Suppl 2):S476-S483. doi: 10.1134/S1064562423701612

Author

Osipov, V. T. ; Gongola, M. I. ; Morkhova, Ye A. et al. / Machine Learning As a Tool to Accelerate the Search for New Materials for Metal-Ion Batteries. In: Doklady Mathematics. 2023 ; Vol. 108, No. Suppl 2. pp. S476-S483.

BibTeX

@article{8628b4113af64e51bd0389b3c78d7fe0,
title = "Machine Learning As a Tool to Accelerate the Search for New Materials for Metal-Ion Batteries",
abstract = "The search for new solid ionic conductors is an important topic of material science that requires significant resources, but can be accelerated using machine learning (ML) techniques. In this work, ML methods were applied to predict the migration energy of working ions. The training set is based on data on 225 lithium ion migration channels in 23 ion conductors. The descriptors were the parameters of free space in the crystal obtained by the Voronoi partitioning method. The accuracy of migration energy prediction was evaluated by comparison with the data obtained by the density functional theory method. Two methods of ML were applied in the work: support vector regression and ordinal regression. It is shown that the parameters of free space in a crystal correlate with the migration energy, while the best results are obtained by ordinal regression. The developed ML models can be used as an additional filter in the analysis of ionic conductivity in solids.",
keywords = "DFT calculations, ToposPro, Voronoi partition, ionic conductors, machine learning, migration energy",
author = "Osipov, {V. T.} and Gongola, {M. I.} and Morkhova, {Ye A.} and Nemudryi, {A. P.} and Kabanov, {A. A.}",
note = "The research was supported by the Russian Science Foundation, project no. 19-73-10026. Публикация для корректировки.",
year = "2023",
month = dec,
doi = "10.1134/S1064562423701612",
language = "English",
volume = "108",
pages = "S476--S483",
journal = "Doklady Mathematics",
issn = "1064-5624",
publisher = "Maik Nauka-Interperiodica Publishing",
number = "Suppl 2",

}

RIS

TY - JOUR

T1 - Machine Learning As a Tool to Accelerate the Search for New Materials for Metal-Ion Batteries

AU - Osipov, V. T.

AU - Gongola, M. I.

AU - Morkhova, Ye A.

AU - Nemudryi, A. P.

AU - Kabanov, A. A.

N1 - The research was supported by the Russian Science Foundation, project no. 19-73-10026. Публикация для корректировки.

PY - 2023/12

Y1 - 2023/12

N2 - The search for new solid ionic conductors is an important topic of material science that requires significant resources, but can be accelerated using machine learning (ML) techniques. In this work, ML methods were applied to predict the migration energy of working ions. The training set is based on data on 225 lithium ion migration channels in 23 ion conductors. The descriptors were the parameters of free space in the crystal obtained by the Voronoi partitioning method. The accuracy of migration energy prediction was evaluated by comparison with the data obtained by the density functional theory method. Two methods of ML were applied in the work: support vector regression and ordinal regression. It is shown that the parameters of free space in a crystal correlate with the migration energy, while the best results are obtained by ordinal regression. The developed ML models can be used as an additional filter in the analysis of ionic conductivity in solids.

AB - The search for new solid ionic conductors is an important topic of material science that requires significant resources, but can be accelerated using machine learning (ML) techniques. In this work, ML methods were applied to predict the migration energy of working ions. The training set is based on data on 225 lithium ion migration channels in 23 ion conductors. The descriptors were the parameters of free space in the crystal obtained by the Voronoi partitioning method. The accuracy of migration energy prediction was evaluated by comparison with the data obtained by the density functional theory method. Two methods of ML were applied in the work: support vector regression and ordinal regression. It is shown that the parameters of free space in a crystal correlate with the migration energy, while the best results are obtained by ordinal regression. The developed ML models can be used as an additional filter in the analysis of ionic conductivity in solids.

KW - DFT calculations

KW - ToposPro

KW - Voronoi partition

KW - ionic conductors

KW - machine learning

KW - migration energy

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85188630100&origin=inward&txGid=091cfe484ca1169669e3bc7e42581c88

UR - https://www.mendeley.com/catalogue/0dd3ddb9-2a7c-3eed-be17-1bf86788d5e5/

U2 - 10.1134/S1064562423701612

DO - 10.1134/S1064562423701612

M3 - Article

VL - 108

SP - S476-S483

JO - Doklady Mathematics

JF - Doklady Mathematics

SN - 1064-5624

IS - Suppl 2

ER -

ID: 59887986