Machine learning prediction of the internal conversion rate constant
For the first time, a model for predicting the internal conversion rate constant (S1→S0) was developed using the machine learning method «random forest», based on a series of porphyrins with various substituents. The model was trained on 1058 molecules, while 256 molecular structures were utilized for testing. The constructed model employs molecular geometric parameters as input data, including bond lengths, as well as valence and dihedral angles. The results obtained indicate a correlation between the geometric parameters of the molecule and the internal conversion rate constant. The primary molecular descriptors for the studied porphyrins are the bond lengths and angles within the porphyrin macrocycle.
Keywords
porphyrinoids,
quantum chemistry,
machine learning,
internal conversion rate constant,
electron transitionAuthors
| Valiulina Lenara I. | Tomsk State University | valiulina-lenara@mail.ru |
| Valiev Rashid R. | Tomsk State University | valievrashid@mail.ru |
| Nasibullin Rinat T. | Tomsk State University | nasibullin.rt1995@gmail.com |
| Cherepanov Victor N. | Tomsk State University | vnch1626@mail.ru |
Всего: 4
References
Keith J.A., Vassilev-Galindo V., Cheng B., et al. // Chem. Rev. - 2021. - V. 121. - P. 9816-9872.
Pereira F., Xiao K., Latino D.A.R.S., et al. // J. Chem. Inf. Model. - 2017. - V. 57. - P. 11-21.
Mazouin B., Schöpfer A.A., von Lilienfeld O.A. // Mater. Adv. - 2022. - V. 3. - P. 8306-8316.
Stein H.S., Guevarra D., Newhouse P.F., et al. // Chem. Sci. - 2019. - V. 10. - P. 47-55.
Jung S.G., Jung G., Cole J.M. // J. Chem. Inf. Model. - 2024. - V. 64. - P. 1486-1501.
Valeur B. Molecular Fluorescence: Principles and Applications. - Weinheim (Federal Republic of Germany): Wiley-VCH GmbH, 2002.
Valiev R.R., Cherepanov V.N., Baryshnikov G.V., et al. // Phys. Chem. Chem. Phys. - 2018. - V. 20. - P. 6121-6133.
Valiev R.R., Merzlikin B.S., Nasibullin R.T., et al. // Phys. Chem. Chem. Phys. - 2023. - V. 25. - P. 6406-6415.
Nasibullin R.T., Merzlikin B.S., Valiev R.R., et al. // Chem. Phys. Lett. - 2024. - V. 840. - P. 141147.
Plotnikov V.G. // Int. J. Quantum Chem. - 1979. - V. 16. - P. 527-541.
Valiev R.R., Nasibullin R.T., Cherepanov V.N., et al. // Phys. Chem. Chem. Phys. - 2020. - V. 22. - P. 22314-22323.
Майер Г.В., Артюхов В.Я., Риб Н.Р. // Изв. вузов. Физика. - 1993. - Т. 36. - № 10. - С. 69-75.
Artyukhov V.Y., Galeeva A.I., Maier G.V., et al. // Opt. Spektrosk. - 1997. - V. 82. - P. 520-523.
Bannwarth Ch., Caldeweyher E., Ehlert S., et al. // WIREs Comp. Molec. Sci. - 2021. - V. 11. - P. e1493.
Grimme S. // J. Chem. Phys. - 2013. - V. 138. - Art. 244104.
Grimme S., Bannwarth Ch. // J. Chem. Phys. - 2016. - V. 145. - Art. 054103.
Mahmood A., Hu J.-Y., Xiao B., et al. // J. Mater. Chem. A. - 2018. - V. 6. - P. 16769-16797.
Leo B. // Machine Learning. - 2001. - V. 45. - P. 5-32.
https://cmr.fysik.dtu.dk/.
Valiev R.R., Cherepanov V.N., Artyukhov V.Ya., et al. // Phys. Chem. Chem. Phys. - 2012. - V. 14. - P. 11508-11517.
Moriwaki H., Tian Y.-S., Kawashita N., et al. // J. Cheminform. - 2018. - V. 10(1). - P. 4.
O’Boyle N. M., Banck M., James C. A., et al. // J. Cheminform. - 2011. - V. 3(1). - P. 33.
Ørnsø K.B., Pedersen Ch.S., Garcia-Lastra J.M., et al. // Phys. Chem. Chem. Phys. - 2014. - V. 16. - P. 16246-16254.
Ørnsø K.B., Garcia-Lastra J.M., Thygesen K.S. // Phys. Chem. Chem. Phys. - 2013. - V. 15. - P. 19478-19486.
Yella A., Lee H.-W., Tsao H.N., et al. // Science. - 2011. - V. 334. - P. 629-634.
Liu B., Zhu W., Wang Y., et al. //J. Mater. Chem. - 2012. - V. 22. - P. 7434-7444.
