- PII
- 10.31857/S0207401X24120041-1
- DOI
- 10.31857/S0207401X24120041
- Publication type
- Article
- Status
- Published
- Authors
- Volume/ Edition
- Volume 43 / Issue number 12
- Pages
- 40-52
- Abstract
- Modeling of the optical response of photosynthetic pigments is an essential part of the study of fundamental physical processes of interaction of multi-atomic molecules with an external electromagnetic field. The use of semiclassical quantum theories in this case is more preferable than the use of ab initio methods for calculating the ground and excited states of a molecule, since semiclassical theories allow us to use characteristic functions, such as spectral density, to calculate absorption spectra rather than to take into account the full set of electron and atom configurations. The main disadvantage of this approach is the necessity of constant comparison of the calculated and experimental spectra and, as a consequence, the need to justify the uniqueness of the obtained parameters of the system under study and to evaluate their statistical significance. One of the possible options to significantly improve the quality of the optical response calculation is the use of a heuristic evolutionary optimization algorithm that minimizes the difference between the measured and theoretical spectra by determining the most appropriate set of model parameters. Using the spectra of photosynthetic pigments measured in different solvents as an example, we have shown that the modeling optimization not only allows us to obtain a good agreement between the calculated and experimental data, but also to unambiguously determine such parameters of the theory as the electron-phonon interaction coefficients for the electronic excited states of chlorophyll, lutein and β-carotene.
- Keywords
- хлорофилл a лютеин β-каротин спектральная плотность теория многомодовых броуновских осцилляторов алгоритмы оптимизации дифференциальная эволюция
- Date of publication
- 14.09.2025
- Year of publication
- 2025
- Number of purchasers
- 0
- Views
- 2
References
- 1. Jang S.J., Mennucci B. // Rev. Mod. Phys. 2018. V. 90. № 035003. https://doi.org/10.1103/RevModPhys.90.035003
- 2. Mirkovic T., Ostroumov E.E., Anna J.M. et al. // Chem. Rev. 2017. V. 117. № 2. P. 249. https://doi.org/10.1021/acs.chemrev.6b00002
- 3. Горохов В.В., Нокс П.П., Корватовский Б.Н. и др. // Хим. физика. 2023. Т. 42. № 6. С. 63. https://doi.org/10.31857/S0207401X23060055
- 4. Blankenship R. E. Molecular Mechanisms of Photosynthesis. 2nd ed., Oxford: Wiley-Blackwell, 2014.
- 5. Renger T., Madjet M.E.A., Busch M.S.A. et al. // Photosynth. Res. 2013. V. 116. P. 367. https://doi.org/10.1007/s11120-013-9893-3
- 6. Черепанов Д.А., Милановский Г.Е., Айбуш А.В. и др. // Хим. физика. 2023. Т. 42. № 6. С. 77. https://doi.org/10.31857/S0207401X23060031
- 7. Renger T. // J. Phys. Chem. B. 2021. V. 125. № 4. P. 6406. https://doi.org/10.1021/acs.jpcb.1c01479
- 8. Novoderezhkin V. I., Romero E., Dekker J.P. et al. // ChemPhysChem. 2011. V. 12. № 3. P. 681. https://doi.org/10.1002/cphc.201000830
- 9. Bruggemann B., Sznee K., Novoderezhkin V. et al. // J. Phys. Chem. B. 2004. V. 108. № 35. P. 13536. https://doi.org/10.1021/jp0401473
- 10. Brixner T., Hildner R., Kohler J. et al. // Adv. Energy Mater. 2017. V. 7. № 16. P. 1700236. https://doi.org/10.1002/aenm.201700236
- 11. Croce R., van Amerongen H. // Nat. Chem. Biol. 2014. V. 10. P. 492. https://doi.org/10.1038/nchembio.1555
- 12. Черепанов Д.А., Милановский Г.Е., Надточенко В.А. и др. // Хим. физика. 2023. Т. 42. № 6. С. 88. https://doi.org/10.31857/S0207401X23060043
- 13. Nelson T.R., White A.J., Bjorgaard J.A. et al. // Chem. Rev. 2020. V. 120. № 4. Р. 2215. https://doi.org/10.1021/acs.chemrev.9b00447
- 14. Cremer D., Pople J.A. // J. Amer. Chem. Soc. 1975. V. 97. № 6. P. 1354. https://doi.org/10.1021/ja00839a011
- 15. Ditchfield R., Hehre W.J., Pople J.A. // J. Chem. Phys. 1971. V. 54. № 2. P. 724. https://doi.org/10.1063/1.1674902
- 16. Хренова М.Г., Поляков И.В., Немухин А.В. // Хим. физика. 2022. Т. 41. № 6. С. 65. https://doi.org/10.31857/S0207401X22060061
- 17. Mukamel S. Principles of Nonlinear Optical Spectroscopy. New York, Oxford: Oxford University Press, 1995.
- 18. Chesalin D.D., Kulikov E.A., Yaroshevich I.A. et al. // Swarm Evol. Comput. 2022. V. 75. № 101210. https://doi.org/10.1016/j.swevo.2022.101210
- 19. Storn R. // IEEE Trans. Evol. Comput. 1999. V. 3. № 1. P. 22. https://doi.org/10.1109/4235.752918
- 20. Storn R., Price K. // J. Global Optim. 1997. V. 11. P. 341. https://doi.org/10.1023/A:1008202821328
- 21. Opara K.R., Arabas J. // Swarm Evol. Comput. 2019. V. 44. P. 546. https://doi.org/10.1016/j.swevo.2018.06.010
- 22. Гудков С.В., Саримов Р.М., Асташев М.Е. и др. // УФН. 2024. Т. 194 С. 208. https://doi.org/10.3367/UFNr.2023.09.039577
- 23. Pishchalnikov R.Y., Yaroshevich I.A., Zlenko D.V. et al. // Photosynth. Res. 2023. V. 156. № 1. P. 3. https://doi.org/10.1007/s11120-022-00955-2
- 24. Pishchalnikov R.Y., Yaroshevich I.A., Slastnikova T.A. et al. // Phys. Chem. Chem. Phys. 2019. V. 21. № 46. Р. 25707. https://doi.org/10.1039/c9cp04508b
- 25. Balevičius V., Abramavicius D., Polívka T. // J. Phys. Chem. Lett. 2016. V. 7. № 17. P. 3347. https://doi.org/10.1021/acs.jpclett.6b01455
- 26. Uragami C., Saito K., Yoshizawa M., Molnar P. et al. // Arch. Biochem. Biophys. 2018. V. 650. P. 49. https://doi.org/10.1016/j.abb.2018.04.021
