Open Access
RSC Advances, volume 14, issue 9, pages 6385-6397
Predicting band gaps of ABN3 perovskites: an account from machine learning and first-principle DFT studies
Publication type: Journal Article
Publication date: 2024-02-20
Journal:
RSC Advances
Quartile SCImago
Q2
Quartile WOS
Q2
Impact factor: 3.9
ISSN: 20462069, 20462069
General Chemistry
General Chemical Engineering
Abstract
A combined machine learning and DFT studies in predicting band gaps of ABN3 perovskites.
Top-30
Citations by journals
1
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Russian Chemical Reviews
1 publication, 50%
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Computational Materials Science
1 publication, 50%
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1
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Citations by publishers
1
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Autonomous Non-profit Organization Editorial Board of the journal Uspekhi Khimii
1 publication, 50%
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Elsevier
1 publication, 50%
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1
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- Statistics recalculated weekly.
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Ghosh S., Chowdhury J. Predicting band gaps of ABN3 perovskites: an account from machine learning and first-principle DFT studies // RSC Advances. 2024. Vol. 14. No. 9. pp. 6385-6397.
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Ghosh S., Chowdhury J. Predicting band gaps of ABN3 perovskites: an account from machine learning and first-principle DFT studies // RSC Advances. 2024. Vol. 14. No. 9. pp. 6385-6397.
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TY - JOUR
DO - 10.1039/d4ra00402g
UR - https://doi.org/10.1039/d4ra00402g
TI - Predicting band gaps of ABN3 perovskites: an account from machine learning and first-principle DFT studies
T2 - RSC Advances
AU - Ghosh, Swarup
AU - Chowdhury, Joydeep
PY - 2024
DA - 2024/02/20 00:00:00
PB - Royal Society of Chemistry (RSC)
SP - 6385-6397
IS - 9
VL - 14
SN - 2046-2069
SN - 2046-2069
ER -
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@article{2024_Ghosh,
author = {Swarup Ghosh and Joydeep Chowdhury},
title = {Predicting band gaps of ABN3 perovskites: an account from machine learning and first-principle DFT studies},
journal = {RSC Advances},
year = {2024},
volume = {14},
publisher = {Royal Society of Chemistry (RSC)},
month = {feb},
url = {https://doi.org/10.1039/d4ra00402g},
number = {9},
pages = {6385--6397},
doi = {10.1039/d4ra00402g}
}
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MLA
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Ghosh, Swarup, and Joydeep Chowdhury. “Predicting band gaps of ABN3 perovskites: an account from machine learning and first-principle DFT studies.” RSC Advances, vol. 14, no. 9, Feb. 2024, pp. 6385-6397. https://doi.org/10.1039/d4ra00402g.