Chemistry of Materials

, том 32 , издание 18 , страницы 7822-7831

Transferable and Extensible Machine Learning-Derived Atomic Charges for Modeling Hybrid Nanoporous Materials

Тип публикации: Journal Article

Дата публикации: 2020-08-25

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American Chemical Society (ACS)

Chemistry of Materials

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SJR: 2.065

CiteScore: 12

Impact factor: 7

ISSN: 08974756, 15205002

DOI: 10.1021/acs.chemmater.0c02468

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Materials Chemistry

General Chemistry

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Nanoporous materials have attracted significant interest as an emerging platform for adsorption-related applications. The high-throughput computational screening became a standard technique to acce...

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Korolev V. et al. Transferable and Extensible Machine Learning-Derived Atomic Charges for Modeling Hybrid Nanoporous Materials // Chemistry of Materials. 2020. Vol. 32. No. 18. pp. 7822-7831.

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Korolev V., Mitrofanov A., Marchenko E. I., Eremin N. N., Tkachenko V., Kalmykov S. N. Transferable and Extensible Machine Learning-Derived Atomic Charges for Modeling Hybrid Nanoporous Materials // Chemistry of Materials. 2020. Vol. 32. No. 18. pp. 7822-7831.

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TY - JOUR

DO - 10.1021/acs.chemmater.0c02468

UR - https://pubs.acs.org/doi/10.1021/acs.chemmater.0c02468

TI - Transferable and Extensible Machine Learning-Derived Atomic Charges for Modeling Hybrid Nanoporous Materials

T2 - Chemistry of Materials

AU - Korolev, Vadim

AU - Mitrofanov, Artem

AU - Marchenko, Ekaterina I

AU - Eremin, Nickolay N

AU - Tkachenko, Valery

AU - Kalmykov, Stepan N.

PY - 2020

DA - 2020/08/25

PB - American Chemical Society (ACS)

SP - 7822-7831

IS - 18

VL - 32

SN - 0897-4756

SN - 1520-5002

ER -

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@article{2020_Korolev,

author = {Vadim Korolev and Artem Mitrofanov and Ekaterina I Marchenko and Nickolay N Eremin and Valery Tkachenko and Stepan N. Kalmykov},

title = {Transferable and Extensible Machine Learning-Derived Atomic Charges for Modeling Hybrid Nanoporous Materials},

journal = {Chemistry of Materials},

year = {2020},

volume = {32},

publisher = {American Chemical Society (ACS)},

month = {aug},

url = {https://pubs.acs.org/doi/10.1021/acs.chemmater.0c02468},

number = {18},

pages = {7822--7831},

doi = {10.1021/acs.chemmater.0c02468}

}

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Korolev, Vadim, et al. “Transferable and Extensible Machine Learning-Derived Atomic Charges for Modeling Hybrid Nanoporous Materials.” Chemistry of Materials, vol. 32, no. 18, Aug. 2020, pp. 7822-7831. https://pubs.acs.org/doi/10.1021/acs.chemmater.0c02468.