Casanola-Martin, Gerardo M, Wang, Jing, Zhou, Jian-ge, Rasulev, Bakhtiyor, and Leszczynski, Jerzy. Chemical feature-based machine learning model for predicting photophysical properties of BODIPY compounds: density functional theory and quantitative structure–property relationship modeling. Retrieved from https://par.nsf.gov/biblio/10560406. Journal of Molecular Modeling 31.1 Web. doi:10.1007/s00894-024-06240-4.

Casanola-Martin, Gerardo M, Wang, Jing, Zhou, Jian-ge, Rasulev, Bakhtiyor, & Leszczynski, Jerzy. Chemical feature-based machine learning model for predicting photophysical properties of BODIPY compounds: density functional theory and quantitative structure–property relationship modeling. Journal of Molecular Modeling, 31 (1). Retrieved from https://par.nsf.gov/biblio/10560406. https://doi.org/10.1007/s00894-024-06240-4

Casanola-Martin, Gerardo M, Wang, Jing, Zhou, Jian-ge, Rasulev, Bakhtiyor, and Leszczynski, Jerzy. "Chemical feature-based machine learning model for predicting photophysical properties of BODIPY compounds: density functional theory and quantitative structure–property relationship modeling". Journal of Molecular Modeling 31 (1). Country unknown/Code not available: Springer. https://doi.org/10.1007/s00894-024-06240-4. https://par.nsf.gov/biblio/10560406.

@article{osti_10560406,
place = {Country unknown/Code not available}, title = {Chemical feature-based machine learning model for predicting photophysical properties of BODIPY compounds: density functional theory and quantitative structure–property relationship modeling}, url = {https://par.nsf.gov/biblio/10560406}, DOI = {10.1007/s00894-024-06240-4}, abstractNote = {}, journal = {Journal of Molecular Modeling}, volume = {31}, number = {1}, publisher = {Springer}, author = {Casanola-Martin, Gerardo M and Wang, Jing and Zhou, Jian-ge and Rasulev, Bakhtiyor and Leszczynski, Jerzy}, }