Using Graph Neural Networks to Predict Positions of the Absorption Maxima of a Number of Dyes
Publication type: Journal Article
Publication date: 2024-12-01
scimago Q4
wos Q4
SJR: 0.204
CiteScore: 1.2
Impact factor: 0.8
ISSN: 00360244, 1531863X
Abstract
Results are presented from developing a model for accurately predicting the wavelength of the absorption maximum of boron(III) dipyrromethenates (BODIPYs). The model is based on a graph neural network (GNN) and includes data for >2500 dyes of various natures. Statistical parameters of the model (MAE and R2) are 4 nm and 0.99 for the training set and 13.5 nm and 0.87 for the testing set. The developed model is available to the public in the GitHub repository ( https://github.com/lukanov-9b/Abs_model.git ).
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Lukanov M. M. et al. Using Graph Neural Networks to Predict Positions of the Absorption Maxima of a Number of Dyes // Russian Journal of Physical Chemistry A. 2024. Vol. 98. No. 14. pp. 3342-3346.
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Lukanov M. M., Ksenofontov A. A. Using Graph Neural Networks to Predict Positions of the Absorption Maxima of a Number of Dyes // Russian Journal of Physical Chemistry A. 2024. Vol. 98. No. 14. pp. 3342-3346.
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TY - JOUR
DO - 10.1134/s003602442470287x
UR - https://link.springer.com/10.1134/S003602442470287X
TI - Using Graph Neural Networks to Predict Positions of the Absorption Maxima of a Number of Dyes
T2 - Russian Journal of Physical Chemistry A
AU - Lukanov, M M
AU - Ksenofontov, A A
PY - 2024
DA - 2024/12/01
PB - Pleiades Publishing
SP - 3342-3346
IS - 14
VL - 98
SN - 0036-0244
SN - 1531-863X
ER -
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@article{2024_Lukanov,
author = {M M Lukanov and A A Ksenofontov},
title = {Using Graph Neural Networks to Predict Positions of the Absorption Maxima of a Number of Dyes},
journal = {Russian Journal of Physical Chemistry A},
year = {2024},
volume = {98},
publisher = {Pleiades Publishing},
month = {dec},
url = {https://link.springer.com/10.1134/S003602442470287X},
number = {14},
pages = {3342--3346},
doi = {10.1134/s003602442470287x}
}
Cite this
MLA
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Lukanov, M. M., et al. “Using Graph Neural Networks to Predict Positions of the Absorption Maxima of a Number of Dyes.” Russian Journal of Physical Chemistry A, vol. 98, no. 14, Dec. 2024, pp. 3342-3346. https://link.springer.com/10.1134/S003602442470287X.
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