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An efficient method of searching for correlations between unlimited datasets to provide forecasting models

Alexander Yurevich Tolbin 1
Alexander Yurevich Tolbin
1 Institute of Physiologically Active Compounds, Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, Chernogolovka, Moscow Region, Russian Federation
10.1016/j.mencom.2023.04.038
Published 2023-04-13
Communication , Volume 33, Issue 3, 419-421
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Tolbin A. Y. An efficient method of searching for correlations between unlimited datasets to provide forecasting models // Mendeleev Communications. 2023. Vol. 33. No. 3. pp. 419-421.
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Tolbin A. Y. An efficient method of searching for correlations between unlimited datasets to provide forecasting models // Mendeleev Communications. 2023. Vol. 33. No. 3. pp. 419-421.
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TY - JOUR
DO - 10.1016/j.mencom.2023.04.038
UR - https://mendcomm.colab.ws/publications/10.1016/j.mencom.2023.04.038
TI - An efficient method of searching for correlations between unlimited datasets to provide forecasting models
T2 - Mendeleev Communications
AU - Tolbin, Alexander Yurevich
PY - 2023
DA - 2023/04/13
PB - Mendeleev Communications
SP - 419-421
IS - 3
VL - 33
ER -
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@article{2023_Tolbin,
author = {Alexander Yurevich Tolbin},
title = {An efficient method of searching for correlations between unlimited datasets to provide forecasting models},
journal = {Mendeleev Communications},
year = {2023},
volume = {33},
publisher = {Mendeleev Communications},
month = {Apr},
url = {https://mendcomm.colab.ws/publications/10.1016/j.mencom.2023.04.038},
number = {3},
pages = {419--421},
doi = {10.1016/j.mencom.2023.04.038}
}
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Tolbin, Alexander Yurevich. “An efficient method of searching for correlations between unlimited datasets to provide forecasting models.” Mendeleev Communications, vol. 33, no. 3, Apr. 2023, pp. 419-421. https://mendcomm.colab.ws/publications/10.1016/j.mencom.2023.04.038.

Keywords

correlations
forecasting models.
J-dimers
optical limiting
phthalocyanine

Abstract

This work presents a highly powerful and very simple statistical method for creating predictive models based on structure-property relationships. The algorithm was implemented in the software CORRELATO, which demo version is presented in Online Supplementary Materials. This algorithm was tested on a small series of phthalocyanine dyes to search for the relationship between optical limiting effect and quantum chemical descriptors responsible for the nonlinear optical properties of these absorbers

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