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Chemical feature-based machine learning model for predicting photophysical properties of BODIPY compounds: density functional theory and quantitative structure–property relationship modeling

Crossref DOI link: https://doi.org/10.1007/s00894-024-06240-4

Published Online: 2024-12-12

Published Print: 2025-01

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Casanola-Martin, Gerardo M.

Wang, Jing

Zhou, Jian-ge

Rasulev, Bakhtiyor

Leszczynski, Jerzy
Funding

Funding for this research was provided by:

National Science Foundation (IIA-1355466)

National Science Foundation (OIA-1757220)

National Science Foundation (EES-2300321)

National Science Foundation (IIA-1355466)

National Science Foundation (OIA-1757220)

U.S. Department of Energy (DE-SC0022239)

U.S. Department of Energy (DE-SC0022239)
License Information

Text and Data Mining valid from 2024-12-12

Version of Record valid from 2024-12-12
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Article History

Received: 15 August 2024

Accepted: 28 November 2024

First Online: 12 December 2024

Declarations

:

: The authors declare no competing interests.

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