Chemistry of Materials, volume 32, issue 18, pages 7822-7831
Transferable and Extensible Machine Learning-Derived Atomic Charges for Modeling Hybrid Nanoporous Materials
Korolev Vadim
1, 2
,
Mitrofanov Artem
1, 2
,
Marchenko Ekaterina I
3, 4
,
Eremin Nickolay N
4
,
Tkachenko Valery
1
,
1
Science Data Software, LLC, 14909 Forest Landing Circle, Rockville, Maryland 20850, United States
|
Publication type: Journal Article
Publication date: 2020-08-25
Journal:
Chemistry of Materials
Quartile SCImago
Q1
Quartile WOS
Q1
Impact factor: 8.6
ISSN: 08974756, 15205002
Materials Chemistry
General Chemistry
General Chemical Engineering
Abstract
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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1 publication, 3.85%
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- We do not take into account publications that without a DOI.
- Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
- Statistics recalculated weekly.
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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://doi.org/10.1021%2Facs.chemmater.0c02468
TI - Transferable and Extensible Machine Learning-Derived Atomic Charges for Modeling Hybrid Nanoporous Materials
T2 - Chemistry of Materials
AU - Marchenko, Ekaterina I
AU - Eremin, Nickolay N
AU - Tkachenko, Valery
AU - Korolev, Vadim
AU - Mitrofanov, Artem
AU - Kalmykov, Stepan N.
PY - 2020
DA - 2020/08/25 00:00:00
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 = {Ekaterina I Marchenko and Nickolay N Eremin and Valery Tkachenko and Vadim Korolev and Artem Mitrofanov 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://doi.org/10.1021%2Facs.chemmater.0c02468},
number = {18},
pages = {7822--7831},
doi = {10.1021/acs.chemmater.0c02468}
}
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MLA
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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://doi.org/10.1021%2Facs.chemmater.0c02468.