Materials Horizons, volume 7, issue 10, pages 2710-2718
Machine-learning-assisted search for functional materials over extended chemical space
Publication type: Journal Article
Publication date: 2020-08-04
Journal:
Materials Horizons
Quartile SCImago
Q1
Quartile WOS
Q1
Impact factor: 13.3
ISSN: 20516347, 20516355
Process Chemistry and Technology
General Materials Science
Electrical and Electronic Engineering
Mechanics of Materials
Abstract
New computational framework has extended an inverse materials design over all the possible stoichiometric compounds.
Citations by journals
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1
2
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Chemistry of Materials
|
Chemistry of Materials
2 publications, 14.29%
|
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Digital Discovery
|
Digital Discovery
2 publications, 14.29%
|
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Journal of Computational Chemistry
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Journal of Computational Chemistry
1 publication, 7.14%
|
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Scientific Reports
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Scientific Reports
1 publication, 7.14%
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npj Computational Materials
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npj Computational Materials
1 publication, 7.14%
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Matter
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Matter
1 publication, 7.14%
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Advanced Science
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Advanced Science
1 publication, 7.14%
|
|
Journal of Chemical Information and Modeling
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Journal of Chemical Information and Modeling
1 publication, 7.14%
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Patterns
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Patterns
1 publication, 7.14%
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|
Nature Communications
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Nature Communications
1 publication, 7.14%
|
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Chemometrics and Intelligent Laboratory Systems
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Chemometrics and Intelligent Laboratory Systems
1 publication, 7.14%
|
|
Materials Horizons
|
Materials Horizons
1 publication, 7.14%
|
|
1
2
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Citations by publishers
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1
2
3
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American Chemical Society (ACS)
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American Chemical Society (ACS)
3 publications, 21.43%
|
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Springer Nature
|
Springer Nature
3 publications, 21.43%
|
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Elsevier
|
Elsevier
3 publications, 21.43%
|
|
Royal Society of Chemistry (RSC)
|
Royal Society of Chemistry (RSC)
3 publications, 21.43%
|
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Wiley
|
Wiley
2 publications, 14.29%
|
|
1
2
3
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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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GOST
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Korolev V. et al. Machine-learning-assisted search for functional materials over extended chemical space // Materials Horizons. 2020. Vol. 7. No. 10. pp. 2710-2718.
GOST all authors (up to 50)
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Korolev V., Mitrofanov A., Eliseev A., Tkachenko V. Machine-learning-assisted search for functional materials over extended chemical space // Materials Horizons. 2020. Vol. 7. No. 10. pp. 2710-2718.
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TY - JOUR
DO - 10.1039/d0mh00881h
UR - https://doi.org/10.1039%2Fd0mh00881h
TI - Machine-learning-assisted search for functional materials over extended chemical space
T2 - Materials Horizons
AU - Korolev, Vadim
AU - Mitrofanov, Artem
AU - Eliseev, Artem
AU - Tkachenko, Valery
PY - 2020
DA - 2020/08/04 00:00:00
PB - Royal Society of Chemistry (RSC)
SP - 2710-2718
IS - 10
VL - 7
SN - 2051-6347
SN - 2051-6355
ER -
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BibTex
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@article{2020_Korolev,
author = {Vadim Korolev and Artem Mitrofanov and Artem Eliseev and Valery Tkachenko},
title = {Machine-learning-assisted search for functional materials over extended chemical space},
journal = {Materials Horizons},
year = {2020},
volume = {7},
publisher = {Royal Society of Chemistry (RSC)},
month = {aug},
url = {https://doi.org/10.1039%2Fd0mh00881h},
number = {10},
pages = {2710--2718},
doi = {10.1039/d0mh00881h}
}
Cite this
MLA
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Korolev, Vadim, et al. “Machine-learning-assisted search for functional materials over extended chemical space.” Materials Horizons, vol. 7, no. 10, Aug. 2020, pp. 2710-2718. https://doi.org/10.1039%2Fd0mh00881h.